EVALUATING
THE PERFORMANCE OF ASSOCIATION RULES IN
APRIORI AND
FP-GROWTH ALGORITHMS: MARKET BASKET
ANALYSIS TO
DISCOVERRULES OF ITEM COMBINATIONS
Dedy Dwiputra1, Agung Mulyo Widodo2,
Habibullah Akbar3, Gerry Firmansyah4
Universitas Esa Unggul, Jakarta, Indonesia
�[email protected]1, [email protected]2, [email protected]3, [email protected]4
ABSTRACT
This
study focuses on applying data mining techniques, especially association rules
mining using the Apriori and FP-GROWTH algorithms, for market basket analysis
on PT. XYZ is a pharmaceutical company in Indonesia. A quantitative methodology
uses a dataset of 100,498 transactions originating from 432,356 rows of data
covering July to December 2022 in the JABODETABEK area. Apriori and FP-GROWTH
algorithms are applied for association rules mining. The results show that FP-GROWTH
has the fastest execution time of 84,655 seconds. However, the memory usage for
the Apriori algorithm is the lowest at 482.32 MiB, with increments of: 0.21
MiB. For the rules generated, the two algorithms, both Apriori and FP-GROWTH,
produce the same number of rules and values of support, confidence, lift,
Bi-Support, Bi-Confidence, and Bi-Lift. In
conclusion, Apriori is recommended for sales datasets if memory usage and ease
of implementation are important. However, if the speed of execution time and a
large amount of data are considered, FP-GROWTH is a better choice because the
execution time is faster for large amounts of data. However, the choice of
algorithm depends on the specific analysis objectives, itemset size, data
scale, and computational capabilities. Results from association rules mining
provide evidence of product popularity, purchasing patterns, and opportunities
for strategic marketing and inventory management. These findings can help PT.
XYZ improves business efficiency, understands customer behavior, and increases
profitability.
Keywords: market
basket analysis, association rules, a priori, fp-growth.
Corresponding Author: Dedy
Dwiputra
E-mail: [email protected]
INTRODUCTION
The need to deeply understand
customers to predict their desires has always been a major ambition for
companies worldwide, especially those in the health sector (Sumarwan,
2014). This has become increasingly
important in recent years due to the Covid-19 pandemic, resulting in increased
competition and technological advances, which are now making this ambition more
achievable (Diandra & Syahputra, 2021).
Consumer behavior is consumer
activity in deciding to buy, use, and consume goods and services purchased,
including customer factors that can lead to their decision whether to buy and
use products (Sudirman
et al., 2020). Every customer has different
needs and tendencies and has different behavior in fulfilling these things (Daliyah, 2020). However, there are different
behaviors to meet their needs. In that case, they still have some things in
common, one of which is to maximize their satisfaction in consuming the
required product or service.
In recent years, transaction data has been commonly
used as an object of research and analysis for researchers (Kurniawan et
al., 2018). This research focuses on a
pharmaceutical company in Indonesia, namely sales transaction data from the
company to consumers, which must also be reprocessed/re-explored to produce
more valuable information. For example, information on goods with the highest
sales. In addition, information can be utilized in connection with the addition
of the stock of these items. In addition, transaction data can be utilized
regarding the relationship of each item purchased in the customer's basket. We
can use that information for an effective product display/range to attract
customer interest. A common application used to analyze customer shopping cart
transaction data is market basket analysis.
Figure
1. GDP of the Chemical, Pharmaceutical and Traditional Medicine Industries
(2010-2021)
The high competition in the pharmaceutical business in
Indonesia has also resulted in pharmaceutical entrepreneurs looking for the
right marketing strategy to increase sales (Sayyid, 2020). One of them, PT XYZ, is a well-known pharmaceutical
company in Indonesia. This pharmaceutical company only sells traditional
medicines.
Products based on patterns of consumer spending habits
are association rules (Alamsyah et al., 2021). Association rules (AR) is the process of finding
patterns, correlations, associations, or causal structures that often occur
from a set of data found in various types of databases such as relational data,
transactional data and other forms of data storage (Dhanalakshmi & Sankari,
2014 ). The association rules method first came from
marketing and is increasingly used in other fields, such as bioinformatics,
nuclear science, pharmacoepidemiology, and geophysics (Alfiqra & Alfizi,
2018). One application of the association rules method is
Market basket analysis. Market Basket Analysis (MBA) is an application of
association rules (AR) often used to analyze consumer buying patterns.
Therefore, this method is often called association rules�market basket analysis
(ARMBA) (Kurniawan et al., 2018 ). The main objective of market basket analysis is to
identify relationships in a set of products, items or categories (Qoniah &
Priandika, 2020). The main objective of market basket analysis in
marketing strategy is to increase sales by understanding customer buying
patterns (Umar et al., 2022). By analyzing transaction data and finding
associations between products purchased, businesses can identify products that
are likely to be sold together and then strategically place those products near
one another in a physical store or on a website. In this way, businesses can
increase sales by tempting customers to buy more products while shopping. In
addition, market basket analysis can also help businesses develop effective
promotional or discount programs to increase sales of less popular products (Kaur &
Kang, 2016).
In this study, the authors will use transaction data
from a pharmaceutical company in Indonesia from July 2022 to December 2022 in
the JABODETABEK area. To conduct experiments using the market basket analysis
method using the a priori algorithm. The Apriori algorithm is a type of
association rule in data mining. The Apriori algorithm is often used in
shopping cart analysis to determine which items consumers frequently purchase
simultaneously (Ariana & Asana,
2013). This research is expected to help companies obtain
consumer transaction data information. It is expected to assist in making
decisions regarding marketing and product sales strategies, especially products
from pharmaceutical companies.
Based on the background described above, companies
need the right marketing strategy to determine the right product marketing
strategy following customer shopping habits with the assumption of variability.
Due to the large amount of transaction data, companies
need help analyzing customer shopping behavior. The data used to analyze
changes in patterns of consumer spending habits in this study are company
transaction data for six months in the JABODETABEK area. So that the process of
finding patterns and knowledge from large and complex data is needed, which is
called data mining. The data mining technique used is association rules, using
algorithms included in the association rules, namely the Apriori, FP-GROWTH,
and ECLAT algorithms. So this study presents the results of association rules
(rules) for six months of consumer shopping transactions. And then, the results
of the association rules (rules) in each period are analyzed using the most
widely accepted Association Rules Evaluation Index Indicator, namely Support
and Confidence.
Meanwhile, Lift, Validity, Conviction, Influence, and
other indicators have gradually been applied in various studies related to
association rules. However, with the growth in the amount of data and large
data types, these indicators need help. One solution is to improve the
association rule evaluation method with Bi-Support, Bi-Confidence and Bi-Lift.
Overall Variability of Association rules (OVAR) is one
of the metrics used to assess the quality of an association rule (Werdiningsih et al.,
2020). OVAR measures the degree of variability of the
support and confidence of an association rule. The lower the OVAR value, the
more stable the association rules are and the better they predict customer
behavior.
Based on the background above, the objective of this
research is to determine and analyze the performance evaluation of association
rules in PT Algorithm Apriori and FP-Growth: basketball market analysis to
discover item combination rules. This analysis is expected to generate
strategies for marketing new products, proper marketing implementation, and
appropriate logistics and inventory management so that the company can benefit
from this research.
METHODS
This research uses CRISP-DM (Cross et
al. for Data Mining) research.
Figure 1. CRISP-DM Diagram
CRISP-DM
is not the only standard in data mining but is currently the most popular (Muhammad, 2019).
Based on the results of data science-pm polling in the period August-September
2020. CRISP-DM is used 2 to 3 times more than the top 4 widely used standards.
Figure
9. Poll Results
Stages of the CRISP-DM method
1.
Business
Understanding Phase: The initial stage in the CRISP-DM methodology is centered
on comprehending the business goals or research objectives to be achieved. In
this phase, we aim to gain a thorough understanding of the business aspects
that underlie this project.
2.
Data
Understanding Phase: The Data Understanding phase in the CRISP-DM methodology
is dedicated to delving deeper into the understanding of the data that will be
the focus of the research. We endeavor to unearth insights and information from
the dataset that will be used in this project.
3.
Data
Preparation Phase: This phase involves a series of meticulous data processing
steps before we embark on further analysis. We make efforts to ensure that the
data to be used is properly prepared for use.
4.
Modeling
Phase: In this phase, we apply the chosen analytical methods to achieve our
research objectives. We employ this approach to uncover relationships and
patterns within the data.
5.
Evaluation
Phase: The primary focus of the Evaluation phase in the CRISP-DM methodology is
to assess the outcomes of the modeling and market basket analysis conducted
using association rules. We evaluate the extent to which these results align
with the predetermined business or research objectives.
In applying the CRISP-DM Methodology,
the data mining process with the association rules technique is carried out in
a non-paid version of the cloud environment from the Google company, namely
Google Collab. Collab allows users to build, run, and share Python code online
and provides free access to computing resources such as CPU, GPU, and TPU.
The association rules process is a
technique in data mining used to discover relationships or associations between
items in a dataset. In this process, there are three main parameters used to control
the quality of the generated association rules, namely min support, min
confidence, and min lift.
1. Min
Support (Support Threshold): Min support is the minimum threshold value for the
frequency of occurrence of an association in the dataset. If an association
does not meet the specified min support value, it is considered insignificant
and will not be included in the final results. The min support value is used to
eliminate associations that occur infrequently.
2. Min
Confidence (Confidence Threshold): Min confidence is the minimum threshold
value for the probability that an association truly occurs. Confidence value
measures the extent to which we can trust that an association will occur based
on the available data. Associations with confidence values below the min
confidence threshold are disregarded.
3. Min
Lift (Lift Threshold): Min lift is the minimum threshold value used to
determine whether an association is a significant relationship or just a
coincidence. Lift measures how much the probability of an association occurring
differs from the probability of both items occurring independently.
Associations with lift values below the min lift threshold are considered
insignificant.
By using these three parameters, the
association rules process can generate more relevant and meaningful association
rules in the dataset, helping data analysts identify important patterns and
potentially providing valuable insights for decision-making.
Figure 17. Implementation Apriori &
FP-GROWTH flow
Evaluation Metrics of Association Rules
In association analysis, there are several important
metrics:
1.
Support:
This measures the extent to which an itemset or association rule appears in the
data. The higher the support, the more frequently the itemset or rule occurs.
The formula is as follows:
2.
Confidence:
Confidence measures how reliable or probable an association rule is. It looks
at how often item B appears together with item A in transactions. The formula
is as follows:
3.
Lift:
Lift measures whether an association rule is better than random chance. Values
above 1 indicate a useful relationship, while values below 1 suggest a less
significant one. The formula is as follows:
4.
Bi-Support:
If both rule A → B and rule �A → �B are strong, then the rule A → B would be very
strong. Thus, we should look for strong evidence to prove these rules are
interesting. So the Support conditions (Bi-support) of the Bi-directional
measure framework.
5.
Bi-confidence:
Confidence typically signifies that when certain itemsets occur, they may lead
to the occurrence of other itemsets. However, we've observed that the
Confidence metric in association rules primarily focuses on the probability of
"B" occurring when "A" occurs but doesn't adequately account
for the relationship between "A" and "B" when "A"
doesn't occur. This limitation renders many mined association rules invalid. To
address the shortcomings of association rules, it's apparent that Confidence
alone doesn't provide a complete depiction and doesn't fully capture the degree
of correlation between itemsets. Therefore, we propose the concept of
"Bi-confidence." The Bi-Confidence formula is as follows:
6.
Bi-lift:
Related research shows that the Lift method helps produce good evaluation
results. However, it is obvious that Lift puts A and B in equivalent positions,
which shows rule A → B is equivalent to B → A. If we accept rule A
→ B, we should also accept rule B → A. However, sometimes it is not
true. For this problem, the paper proposes a Bi-lift measurement method. Since
there is a need to study the relationship of A → B when you want to
evaluate the relationship of (A → B) by Lift (A → B), we introduce
Lift (A → B) to adjust Lift (A → B). The higher Lift (A → B)
is, the better the rule A → B is; conversely, the higher Lift (A → B)
is, the worse the rule A → B is. Therefore, we propose a Bi-lift
measurement method, taking Lift (A → B) as the denominator and Lift (A
→ B) as the numerator to form the ratio of Lift (A → B) to Lift (A
→ B). The Bi-lift formula is as follows:
All of these metrics are used to identify
significant relationships in the data and support business decision-making.
RESULTS AND DISCUSSION
The dataset used in this study consists of 100,497
transactions with 126 items that occurred within 155 days. This data is a sales
transaction dataset from PT. XYZ, which is a pharmaceutical company in
Indonesia. For the discussion, the research results are divided into two,
namely, the evaluation of the association rules algorithm and the results of
the association rules in the form of combination rules between items that will
be used following the objectives of this study.
Algorithm Evaluation Results
In this research, the sales data analysis of PT. XYZ for July 2022 to
December 2022 uses two association rules mining algorithms, Apriori and FP-GROWTH.
The following is a table of the results of the processes carried out by the
researchers on the dataset, as described in Table 8. Dataset information. by
using the Python programming language on Google Colab.
Table 7. Comparison of Apriori and FP-GROWTH Algorithms
|
Criteria |
A priori |
FP-GROWTH |
||
|
Traditional Measure |
New measure (Bi) |
Traditional Measure |
New measure (Bi) |
|
|
The
amount of data obtained |
432,355 lines |
432,355 Lines |
||
|
The
amount of data processed |
100,497 transactions |
100,497 transactions |
||
|
The
execution time of the entire process |
48,293 seconds |
168,488 seconds |
81,623 seconds |
84,655 seconds |
|
Overall
process execution memory usage |
peak memory: 419.48 MiB, increments: 0.01 MiB |
peak memory: 482.32 MiB, increments: 0.21 MiB |
peak memory: 2388.11 MiB, increments: 0.01 MiB |
peak memory: 2393.77 MiB, increments: 0.76 MiB |
|
Execution
Time is just an algorithm process |
1,032 seconds |
133,576 seconds |
1.555 seconds |
2,645 seconds |
|
Algorithm
process execution memory usage |
peak memory: 376.72 MiB, increments: 0.00 MiB |
peak memory: 441.72 MiB, increments: 0.03 MiB |
peak memory: 2451.02 MiB, increments: 0.00 MiB |
peak memory: 2724.30 MiB, increments: 0.00 MiB |
|
The
total amount generated from min
Support=0.075, min Confidence=0.25, min lift=1 |
45 Rules |
45 Rules |
||
|
The
total amount generated from min
Bi-Support = 0.075, min Bi-Confidence = 0.25, and min Bi-lift = 1 |
0 |
29 Rules |
0 |
29 Rules |
|
minimum combination |
1 |
1 |
||
|
maximum
combination |
2 |
2 |
||
|
Effectiveness |
The
Apriori algorithm generates all possible item sets. Then it scans the
database to calculate the support for each item set. |
The
FP-GROWTH algorithm takes a different approach by building a compact FP-Tree
tree structure from a dataset. This avoids explicitly generating candidate
itemsets. |
||
|
Practical |
The
Apriori algorithm is easy to understand and implement. The concept is simple and intuitive. Generates all
association rules that meet the specified support and confidence limits. |
The
FP-GROWTH algorithm requires a deeper understanding and a more complicated
implementation than Apriori. Requires FP-Tree data structures and complex
tree crawling processes. |
||
Moreover, after applying the
Apriori and FP-GROWTH algorithms. Then do the Overall Variability of Association
rules (OVAR). Moreover, where OVAR stands for "Overall Variability of
Association Rules." It is a statistical measure used in data mining and
association rule analysis to quantify the overall variation or dispersion of
support values among discovered association patterns. It helps assess how much
the support values of different association patterns deviate from their average
support value, Mi. OVAR is used to gauge the level of variation or
heterogeneity within the dataset with respect to association patterns. OVAR formula is as follows:
OVAR �
(1/N)*sum(i=1,N) (sum(j=1,K) ((Xij-Mi)˄2))
Where :
N����������� = the number of associatiob patterns discovered by the
algorithm
K����������� = the number of itemsets present in the dataset
Xij��������� = the support of association pattern I and itemset j
Mi��������� = the average support of all association patterns.
The following is a table of
results from the OVAR based on OVAR Formula.
Table 8. Overall Variability of Association Rules (OVAR)
|
Variables |
A priori |
FP-GROWTH |
|
N |
45 |
45 |
|
K |
126 |
126 |
|
Mi |
0.102111 |
0.102103 |
|
Xij |
Value Support 1-45 |
Value Support 1-45 |
|
OVER |
0.000598837 |
0.000598321 |
Table 9. Overall Variability of Association Rules (OVAR)
Bi
|
Variables |
A priori:
Bi-Support |
FP-GROWTH:
Bi-Support |
|
N |
29 |
29 |
|
K |
126 |
126 |
|
Mi |
0.179472 |
0.179457 |
|
Xij |
Support
Value 1-29 |
Support
Value 1-29 |
|
OVER |
0.020236498 |
0.020233159 |
Moreover, the following are
some explanations of the evaluation results of the two algorithms:
1.
The amount of data processed for the three algorithms
is 100,497 transactions, with 126 items in the dataset.
2.
If implementing these association rules uses a
different measure, namely Bi-support, Bi-confidence, and Bi-lift. According to
all criteria, Apriori is the better time and memory usage algorithm.
3.
FP-GROWTH has a faster overall execution time
compared to Apriori. FP-GROWTH takes 84,655 seconds, while Apriori takes
168,488 seconds, almost half of Apriori's processing time. What is interesting
here is that if the execution time is only for implementing the algorithm
without considering the initial load and pre-processing processes, FP-GROWTH
has a very short execution time of 2,645 seconds, while a priori requires
133,576 seconds.
4.
Apriori has almost five times lower memory usage
compared to FP-GROWTH. The peak memory usage for Apriori is 482.32 MiB, while
for FP-GROWTH, it is 2393.77 MiB.
5.
Both methods generate the same number of rules for
both criteria.
6.
In terms of effectiveness, FP-GROWTH is faster in
execution because it reduces the number of database scans and candidate
generation.
7.
Regarding practicality, Apriori has the advantage of
being easy to understand and implement. At the same time, FP-GROWTH is more
complicated but more efficient in large datasets.
In addition, OVAR (Overall Variability of Association rules) calculations
are also carried out with the Mi value calculated from the support value. The
OVAR calculation results show a value of 0.000598837 for Apriori and
0.000598321 for FP-GROWTH. Meanwhile, OVAR based on Bi-Support shows a value of
0.020236498 for Apriori and 0.020233159 for FP-GROWTH.
The results of the rules for
each Apriori and FP-GROWTH algorithm
To analyze
these results, we can look at some of the metrics used by the algorithm:
support, confidence, and lift.
1.
Support measures how often an itemset appears in the dataset
2.
Confidence measures how often the resulting itemsets appear together
3.
Lift measures the dependency between the resulting itemsets
4.
Bi-Support measures how often itemset A and itemset B occur together
with other itemsets in the dataset. Bi-Support considers the relationship
between rules A → B and rules �A → �B and calculates the minimum
value of their support.
5.
Bi-Confidence measures how often rules A → B and rules �A →
�B occur together with other rules in the dataset. Bi-Confidence considers the
relationship between the rules A → B and the rules �A → �B and
calculate the minimum value of the confidence of both.
6.
Bi-Lift measures the strength of the relationship between rules A
→ B and rules �A → �B by considering the relationship between the
itemsets involved. Bi-Lift compares the dependency between the A → B rule
and the �A → �B rule by considering the other itemsets in the dataset.
Using the above metrics, we can
understand how often the itemsets and association rules appear in the dataset,
how strong the relationship between the itemsets and the rules is, and how much
influence there is between the A → B rules and �A → �B rules in the
dataset. In analyzing these results, we can compare the values of support,
confidence, lift, Bi-support, Bi-confidence, and Bi-lift to understand better
the relationship between the itemsets and the association rules in the dataset
used. The results of each algorithm will display the top 15 from each Support,
Confidence, Lift, Bi-Support, Bi-Confidence, and Bi-Lift in each Apriori and FP-GROWTH
algorithm.
Results of Apriori Algorithm
Rules
The following is a table of results from
implementing the a priori algorithm from top to bottom based on the Support
value.
Table
11. Results of Apriori Rules: Rules based on top confidence
|
antecedents |
consequences |
Supp |
Conf |
Elevator |
Bi-Support |
�Bi-Supp |
Bi-Confidence |
Bi-Lift |
|
EUCALYPTUS OIL CAP X 15 |
EUCALYPTUS OIL CAP X 30 |
0.1822 |
0.6438 |
2.1026 |
0.2830 |
0.6938 |
0.4708 |
6.0854 |
|
EUCALYPTUS OIL CAP X 30 |
EUCALYPTUS OIL CAP X 15 |
0.1822 |
0.5949 |
2.1026 |
0.2830 |
0.6938 |
0.4497 |
6.5737 |
|
SRH 60 MASSAGE RUBING OIL |
SRH 30 MASSAGE RUBING OIL |
0.1361 |
0.6308 |
2.0958 |
0.2158 |
0.6990 |
0.4206 |
5.6075 |
|
SRH 30 MASSAGE RUBING OIL |
SRH 60 MASSAGE RUBING OIL |
0.1361 |
0.4523 |
2.0958 |
0.2158 |
0.6990 |
0.3383 |
6.3255 |
|
EUCALYPTUS OIL CAP X 60 |
EUCALYPTUS OIL CAP X 30 |
0.1360 |
0.6179 |
2.0182 |
0.2200 |
0.6938 |
0.3997 |
2.4520 |
|
EUCALYPTUS OIL CAP X 30 |
EUCALYPTUS OIL CAP X 60 |
0.1360 |
0.4441 |
2.0182 |
0.2200 |
0.6938 |
0.3229 |
5.2734 |
|
MUSCLE BALM 10 |
MUSCLE BALM 20 |
0.1206 |
0.6034 |
2.6048 |
0.1999 |
0.7684 |
0.4646 |
3.8435 |
|
MUSCLE BALM 20 |
MUSCLE BALM 10 |
0.1206 |
0.5207 |
2.6048 |
0.1999 |
0.7684 |
0.4175 |
5.1996 |
|
MUSCLE BALM 20 |
SRH 30 MASSAGE RUBING OIL |
0.1181 |
0.5098 |
1.6938 |
0.2316 |
0.6990 |
0.2718 |
2.3418 |
|
SRH 30 MASSAGE RUBING OIL |
MUSCLE BALM 20 |
0.1181 |
0.3923 |
1.6938 |
0.2316 |
0.6990 |
0.2299 |
3.2389 |
|
EUCALYPTUS OIL CAP X 60 |
EUCALYPTUS OIL CAP X 15 |
0.1140 |
0.5181 |
1.8312 |
0.2200 |
0.7170 |
0.3015 |
3.4542 |
|
EUCALYPTUS OIL CAP X 15 |
EUCALYPTUS OIL CAP X 60 |
0.1140 |
0.4029 |
1.8312 |
0.2200 |
0.7170 |
0.2551 |
3.1530 |
|
EUCALYPTUS OIL CAP X 15 |
SRH 30 MASSAGE RUBING OIL |
0.1097 |
0.3876 |
1.2879 |
0.2830 |
0.6990 |
0.1208 |
4.2884 |
|
SRH 30 MASSAGE RUBING OIL |
EUCALYPTUS OIL CAP X 15 |
0.1097 |
0.3644 |
1.2879 |
0.2830 |
0.6990 |
0.1165 |
4.2816 |
|
SRH 30 MASSAGE RUBING OIL |
EUCALYPTUS OIL CAP X 30 |
0.1091 |
0.3624 |
1.1836 |
0.3010 |
0.6938 |
0.0804 |
2.6053 |
|
EUCALYPTUS OIL CAP X 30 |
SRH 30 MASSAGE RUBING OIL |
0.1091 |
0.3562 |
1.1836 |
0.3010 |
0.6938 |
0.0796 |
3.6793 |
|
MUSCLE BALM 10 |
SRH 30 MASSAGE RUBING OIL |
0.1050 |
0.5254 |
1.7454 |
0.1999 |
0.6990 |
0.2804 |
2.5473 |
|
SRH 30 MASSAGE RUBING OIL |
MUSCLE BALM 10 |
0.1050 |
0.3489 |
1.7454 |
0.1999 |
0.6990 |
0.2132 |
3.7167 |
|
SRH 60 MASSAGE RUBING OIL |
MUSCLE BALM 20 |
0.0960 |
0.4449 |
1.9209 |
0.2158 |
0.7684 |
0.2720 |
3.2586 |
|
MUSCLE BALM 20 |
SRH 60 MASSAGE RUBING OIL |
0.0960 |
0.4146 |
1.9209 |
0.2158 |
0.7684 |
0.2586 |
4.8991 |
|
WHITEWOOD OIL CAP X 15, EUCALYPTUS
OIL CAP X 60 |
EUCALYPTUS OIL CAP X 30 |
0.0937 |
0.8222 |
2.6853 |
0.1140 |
0.6938 |
0.5824 |
3.4965 |
|
WHITEWOOD OIL CAP X 60, EUCALYPTUS
OIL CAP X 30 |
EUCALYPTUS OIL CAP X 15 |
0.0937 |
0.6894 |
2.4365 |
0.1360 |
0.7170 |
0.4704 |
4.7302 |
|
WHITEWOOD OIL CAP X 15, EUCALYPTUS
OIL CAP X 30 |
EUCALYPTUS OIL CAP X 60 |
0.0937 |
0.5146 |
2.3387 |
0.1822 |
0.7799 |
0.3602 |
2.8936 |
|
EUCALYPTUS OIL CAP X 60 |
WHITEWOOD OIL CAP X 15, EUCALYPTUS
OIL CAP X 30 |
0.0937 |
0.4260 |
2.3387 |
0.1822 |
0.7799 |
0.3127 |
3.0536 |
|
EUCALYPTUS OIL CAP X 30 |
WHITEWOOD OIL CAP X 15, EUCALYPTUS
OIL CAP X 60 |
0.0937 |
0.3062 |
2.6853 |
0.1140 |
0.6938 |
0.2770 |
3.5603 |
|
EUCALYPTUS OIL CAP X 15 |
WHITEWOOD OIL CAP X 60, EUCALYPTUS
OIL CAP X 30 |
0.0937 |
0.3313 |
2.4365 |
0.1360 |
0.7170 |
0.2724 |
3.0178 |
|
BASLEM 20 |
MUSCLE BALM 20 |
0.0923 |
0.6243 |
2.6954 |
0.1478 |
0.7684 |
0.4608 |
3.3855 |
|
MUSCLE BALM 20 |
BASLEM 20 |
0.0923 |
0.3983 |
2.6954 |
0.1478 |
0.7684 |
0.3261 |
2.7565 |
|
BASLEM 10 |
MUSCLE BALM 10 |
0.0904 |
0.6337 |
3.1696 |
0.1427 |
0.8001 |
0.5059 |
3.1486 |
|
MUSCLE BALM 10 |
BASLEM 10 |
0.0904 |
0.4522 |
3.1696 |
0.1427 |
0.8001 |
0.3869 |
3.9226 |
|
EUCALYPTUS OIL CAP X 60 |
SRH 30 MASSAGE RUBING OIL |
0.0830 |
0.3772 |
1.2531 |
0.2200 |
0.6990 |
0.0977 |
2.5528 |
|
SRH 30 MASSAGE RUBING OIL |
EUCALYPTUS OIL CAP X 60 |
0.0830 |
0.2757 |
1.2531 |
0.2200 |
0.6990 |
0.0797 |
2.9077 |
|
SRH 30 RUBBING OIL, EUCALYPTUS
OIL CAP X 30 |
EUCALYPTUS OIL CAP X 15 |
0.0823 |
0.7549 |
2.6679 |
0.1091 |
0.7170 |
0.5297 |
3.7072 |
|
WHITEWOOD OIL CAP X 15,
SRH 30 RUBBING OIL |
EUCALYPTUS OIL CAP X 30 |
0.0823 |
0.7507 |
2.4518 |
0.1097 |
0.6938 |
0.4993 |
2.6581 |
|
EUCALYPTUS OIL CAP X 15 |
SRH 30 RUBBING OIL, EUCALYPTUS
OIL CAP X 30 |
0.0823 |
0.2910 |
2.6679 |
0.1091 |
0.7170 |
0.2537 |
3.8372 |
|
EUCALYPTUS OIL CAP X 30 |
WHITEWOOD OIL CAP X 15,
SRH 30 RUBBING OIL |
0.0823 |
0.2689 |
2.4518 |
0.1097 |
0.6938 |
0.2295 |
2.8314 |
|
WHITEWOOD OIL CAP X 15, EUCALYPTUS
OIL CAP X 30 |
SRH 30 MASSAGE RUBING OIL |
0.0823 |
0.4520 |
1.5017 |
0.1822 |
0.6990 |
0.1847 |
2.8483 |
|
SRH 30 MASSAGE RUBING OIL |
WHITEWOOD OIL CAP X 15, EUCALYPTUS
OIL CAP X 30 |
0.0823 |
0.2736 |
1.5017 |
0.1822 |
0.6990 |
0.1308 |
2.7171 |
|
MUSCLE BALM 10 |
SRH 60 MASSAGE RUBING OIL |
0.0809 |
0.4046 |
1.8748 |
0.1999 |
0.7842 |
0.2360 |
2.4704 |
|
SRH 60 MASSAGE RUBING OIL |
MUSCLE BALM 10 |
0.0809 |
0.3748 |
1.8748 |
0.1999 |
0.7842 |
0.2230 |
2.3823 |
|
BASLEM 20 |
SRH 30 MASSAGE RUBING OIL |
0.0797 |
0.5395 |
1.7924 |
0.1478 |
0.6990 |
0.2799 |
3.1766 |
|
SRH 30 MASSAGE RUBING OIL |
BASLEM 20 |
0.0797 |
0.2649 |
1.7924 |
0.1478 |
0.6990 |
0.1675 |
2.3051 |
|
BASLEM 10 |
SRH 30 MASSAGE RUBING OIL |
0.0781 |
0.5470 |
1.8174 |
0.1427 |
0.6990 |
0.2870 |
2.5913 |
|
SRH 30 MASSAGE RUBING OIL |
BASLEM 10 |
0.0781 |
0.2593 |
1.8174 |
0.1427 |
0.6990 |
0.1669 |
2.2829 |
|
MUSCLE BALM 20 |
EUCALYPTUS OIL CAP X 30 |
0.0758 |
0.3274 |
1.0692 |
0.2316 |
0.6938 |
0.0276 |
2.9954 |
Then the following is a table
of results from applying the Apriori algorithm for the top to lowest rules
based on the lift value.
Table 12. Results of Apriori Rules: rules based on the
top lift
|
antecedents |
consequences |
Supp |
Conf |
Elevator |
Bi-Support |
�Bi-Supp |
Bi-Confidence |
Bi-Lift |
|
BASLEM 10 |
MUSCLE BALM 10 |
0.0904 |
0.6337 |
3.1696 |
0.1427 |
0.8001 |
0.5059 |
6.0854 |
|
MUSCLE BALM 10 |
BASLEM 10 |
0.0904 |
0.4522 |
3.1696 |
0.1427 |
0.8001 |
0.3869 |
6.5737 |
|
BASLEM 20 |
MUSCLE BALM 20 |
0.0923 |
0.6243 |
2.6954 |
0.1478 |
0.7684 |
0.4608 |
5.6075 |
|
MUSCLE BALM 20 |
BASLEM 20 |
0.0923 |
0.3983 |
2.6954 |
0.1478 |
0.7684 |
0.3261 |
6.3255 |
|
WHITEWOOD OIL CAP X 15, EUCALYPTUS
OIL CAP X 60 |
EUCALYPTUS OIL CAP X 30 |
0.0937 |
0.8222 |
2.6853 |
0.1140 |
0.6938 |
0.5824 |
2.4520 |
|
EUCALYPTUS OIL CAP X 30 |
WHITEWOOD OIL CAP X 15, EUCALYPTUS
OIL CAP X 60 |
0.0937 |
0.3062 |
2.6853 |
0.1140 |
0.6938 |
0.2770 |
5.2734 |
|
SRH 30 RUBBING OIL, EUCALYPTUS
OIL CAP X 30 |
EUCALYPTUS OIL CAP X 15 |
0.0823 |
0.7549 |
2.6679 |
0.1091 |
0.7170 |
0.5297 |
3.8435 |
|
EUCALYPTUS OIL CAP X 15 |
SRH 30 RUBBING OIL, EUCALYPTUS
OIL CAP X 30 |
0.0823 |
0.2910 |
2.6679 |
0.1091 |
0.7170 |
0.2537 |
5.1996 |
|
MUSCLE BALM 10 |
MUSCLE BALM 20 |
0.1206 |
0.6034 |
2.6048 |
0.1999 |
0.7684 |
0.4646 |
2.3418 |
|
MUSCLE BALM 20 |
MUSCLE BALM 10 |
0.1206 |
0.5207 |
2.6048 |
0.1999 |
0.7684 |
0.4175 |
3.2389 |
|
WHITEWOOD OIL CAP X 15,
SRH 30 RUBBING OIL |
EUCALYPTUS OIL CAP X 30 |
0.0823 |
0.7507 |
2.4518 |
0.1097 |
0.6938 |
0.4993 |
3.4542 |
|
EUCALYPTUS OIL CAP X 30 |
WHITEWOOD OIL CAP X 15,
SRH 30 RUBBING OIL |
0.0823 |
0.2689 |
2.4518 |
0.1097 |
0.6938 |
0.2295 |
3.1530 |
|
WHITEWOOD OIL CAP X 60, EUCALYPTUS
OIL CAP X 30 |
EUCALYPTUS OIL CAP X 15 |
0.0937 |
0.6894 |
2.4365 |
0.1360 |
0.7170 |
0.4704 |
4.2884 |
|
EUCALYPTUS OIL CAP X 15 |
WHITEWOOD OIL CAP X 60, EUCALYPTUS
OIL CAP X 30 |
0.0937 |
0.3313 |
2.4365 |
0.1360 |
0.7170 |
0.2724 |
4.2816 |
|
WHITEWOOD OIL CAP X 15, EUCALYPTUS
OIL CAP X 30 |
EUCALYPTUS OIL CAP X 60 |
0.0937 |
0.5146 |
2.3387 |
0.1822 |
0.7799 |
0.3602 |
2.6053 |
|
EUCALYPTUS OIL CAP X 60 |
WHITEWOOD OIL CAP X 15, EUCALYPTUS
OIL CAP X 30 |
0.0937 |
0.4260 |
2.3387 |
0.1822 |
0.7799 |
0.3127 |
3.6793 |
|
EUCALYPTUS OIL CAP X 15 |
EUCALYPTUS OIL CAP X 30 |
0.1822 |
0.6438 |
2.1026 |
0.2830 |
0.6938 |
0.4708 |
2.5473 |
|
EUCALYPTUS OIL CAP X 30 |
EUCALYPTUS OIL CAP X 15 |
0.1822 |
0.5949 |
2.1026 |
0.2830 |
0.6938 |
0.4497 |
3.7167 |
|
SRH 60 MASSAGE RUBING OIL |
SRH 30 MASSAGE RUBING OIL |
0.1361 |
0.6308 |
2.0958 |
0.2158 |
0.6990 |
0.4206 |
3.2586 |
|
SRH 30 MASSAGE RUBING OIL |
SRH 60 MASSAGE RUBING OIL |
0.1361 |
0.4523 |
2.0958 |
0.2158 |
0.6990 |
0.3383 |
4.8991 |
|
EUCALYPTUS OIL CAP X 60 |
EUCALYPTUS OIL CAP X 30 |
0.1360 |
0.6179 |
2.0182 |
0.2200 |
0.6938 |
0.3997 |
3.4965 |
|
EUCALYPTUS OIL CAP X 30 |
EUCALYPTUS OIL CAP X 60 |
0.1360 |
0.4441 |
2.0182 |
0.2200 |
0.6938 |
0.3229 |
4.7302 |
|
SRH 60 MASSAGE RUBING OIL |
MUSCLE BALM 20 |
0.0960 |
0.4449 |
1.9209 |
0.2158 |
0.7684 |
0.2720 |
2.8936 |
|
MUSCLE BALM 20 |
SRH 60 MASSAGE RUBING OIL |
0.0960 |
0.4146 |
1.9209 |
0.2158 |
0.7684 |
0.2586 |
3.0536 |
|
MUSCLE BALM 10 |
SRH 60 MASSAGE RUBING OIL |
0.0809 |
0.4046 |
1.8748 |
0.1999 |
0.7842 |
0.2360 |
3.5603 |
|
SRH 60 MASSAGE RUBING OIL |
MUSCLE BALM 10 |
0.0809 |
0.3748 |
1.8748 |
0.1999 |
0.7842 |
0.2230 |
3.0178 |
|
EUCALYPTUS OIL CAP X 60 |
EUCALYPTUS OIL CAP X 15 |
0.1140 |
0.5181 |
1.8312 |
0.2200 |
0.7170 |
0.3015 |
3.3855 |
|
EUCALYPTUS OIL CAP X 15 |
EUCALYPTUS OIL CAP X 60 |
0.1140 |
0.4029 |
1.8312 |
0.2200 |
0.7170 |
0.2551 |
2.7565 |
|
BASLEM 10 |
SRH 30 MASSAGE RUBING OIL |
0.0781 |
0.5470 |
1.8174 |
0.1427 |
0.6990 |
0.2870 |
3.1486 |
|
SRH 30 MASSAGE RUBING OIL |
BASLEM 10 |
0.0781 |
0.2593 |
1.8174 |
0.1427 |
0.6990 |
0.1669 |
3.9226 |
|
BASLEM 20 |
SRH 30 MASSAGE RUBING OIL |
0.0797 |
0.5395 |
1.7924 |
0.1478 |
0.6990 |
0.2799 |
2.5528 |
|
SRH 30 MASSAGE RUBING OIL |
BASLEM 20 |
0.0797 |
0.2649 |
1.7924 |
0.1478 |
0.6990 |
0.1675 |
2.9077 |
|
MUSCLE BALM 10 |
SRH 30 MASSAGE RUBING OIL |
0.1050 |
0.5254 |
1.7454 |
0.1999 |
0.6990 |
0.2804 |
3.7072 |
|
SRH 30 MASSAGE RUBING OIL |
MUSCLE BALM 10 |
0.1050 |
0.3489 |
1.7454 |
0.1999 |
0.6990 |
0.2132 |
2.6581 |
|
MUSCLE BALM 20 |
SRH 30 MASSAGE RUBING OIL |
0.1181 |
0.5098 |
1.6938 |
0.2316 |
0.6990 |
0.2718 |
3.8372 |
|
SRH 30 MASSAGE RUBING OIL |
MUSCLE BALM 20 |
0.1181 |
0.3923 |
1.6938 |
0.2316 |
0.6990 |
0.2299 |
2.8314 |
|
WHITEWOOD OIL CAP X 15, EUCALYPTUS
OIL CAP X 30 |
SRH 30 MASSAGE RUBING OIL |
0.0823 |
0.4520 |
1.5017 |
0.1822 |
0.6990 |
0.1847 |
2.8483 |
|
SRH 30 MASSAGE RUBING OIL |
WHITEWOOD OIL CAP X 15, EUCALYPTUS
OIL CAP X 30 |
0.0823 |
0.2736 |
1.5017 |
0.1822 |
0.6990 |
0.1308 |
2.7171 |
|
EUCALYPTUS OIL CAP X 15 |
SRH 30 MASSAGE RUBING OIL |
0.1097 |
0.3876 |
1.2879 |
0.2830 |
0.6990 |
0.1208 |
2.4704 |
|
SRH 30 MASSAGE RUBING OIL |
EUCALYPTUS OIL CAP X 15 |
0.1097 |
0.3644 |
1.2879 |
0.2830 |
0.6990 |
0.1165 |
2.3823 |
|
EUCALYPTUS OIL CAP X 60 |
SRH 30 MASSAGE RUBING OIL |
0.0830 |
0.3772 |
1.2531 |
0.2200 |
0.6990 |
0.0977 |
3.1766 |
|
SRH 30 MASSAGE RUBING OIL |
EUCALYPTUS OIL CAP X 60 |
0.0830 |
0.2757 |
1.2531 |
0.2200 |
0.6990 |
0.0797 |
2.3051 |
|
SRH 30 MASSAGE RUBING OIL |
EUCALYPTUS OIL CAP X 30 |
0.1091 |
0.3624 |
1.1836 |
0.3010 |
0.6938 |
0.0804 |
2.5913 |
|
EUCALYPTUS OIL CAP X 30 |
SRH 30 MASSAGE RUBING OIL |
0.1091 |
0.3562 |
1.1836 |
0.3010 |
0.6938 |
0.0796 |
2.2829 |
|
MUSCLE BALM 20 |
EUCALYPTUS OIL CAP X 30 |
0.0758 |
0.3274 |
1.0692 |
0.2316 |
0.6938 |
0.0276 |
2.9954 |
The results of the FP-GROWTH
Algorithm rules
The following is a table of
results from implementing the FP-GROWTH algorithm for the top to lowest rules
based on the Support value.
Table
13. FP-GROWTH Results: Rules based on the Support value
|
antecedents |
consequences |
Supp |
Conf |
Elevator |
Bi-Support |
�Bi-Supp |
Bi-Confidence |
Bi-Lift |
|
EUCALYPTUS OIL CAP X 15 |
EUCALYPTUS OIL CAP X 30 |
0.1822 |
0.6438 |
2.1026 |
0.2830 |
0.6938 |
0.4708 |
6.0854 |
|
EUCALYPTUS OIL CAP X 30 |
EUCALYPTUS OIL CAP X 15 |
0.1822 |
0.5949 |
2.1026 |
0.2830 |
0.6938 |
0.4497 |
6.5737 |
|
SRH 60 MASSAGE RUBING OIL |
SRH 30 MASSAGE RUBING OIL |
0.1361 |
0.6308 |
2.0958 |
0.2158 |
0.6990 |
0.4206 |
5.6075 |
|
SRH 30 MASSAGE RUBING OIL |
SRH 60 MASSAGE RUBING OIL |
0.1361 |
0.4523 |
2.0958 |
0.2158 |
0.6990 |
0.3383 |
6.3255 |
|
EUCALYPTUS OIL CAP X 60 |
EUCALYPTUS OIL CAP X 30 |
0.1360 |
0.6179 |
2.0182 |
0.2200 |
0.6938 |
0.3997 |
2.4520 |
|
EUCALYPTUS OIL CAP X 30 |
EUCALYPTUS OIL CAP X 60 |
0.1360 |
0.4441 |
2.0182 |
0.2200 |
0.6938 |
0.3229 |
5.2734 |
|
MUSCLE BALM 10 |
MUSCLE BALM 20 |
0.1206 |
0.6034 |
2.6048 |
0.1999 |
0.7684 |
0.4646 |
3.8435 |
|
MUSCLE BALM 20 |
MUSCLE BALM 10 |
0.1206 |
0.5207 |
2.6048 |
0.1999 |
0.7684 |
0.4175 |
5.1996 |
|
MUSCLE BALM 20 |
SRH 30 MASSAGE RUBING OIL |
0.1181 |
0.5098 |
1.6938 |
0.2316 |
0.6990 |
0.2718 |
2.3418 |
|
SRH 30 MASSAGE RUBING OIL |
MUSCLE BALM 20 |
0.1181 |
0.3923 |
1.6938 |
0.2316 |
0.6990 |
0.2299 |
3.2389 |
|
EUCALYPTUS OIL CAP X 60 |
EUCALYPTUS OIL CAP X 15 |
0.1140 |
0.5181 |
1.8312 |
0.2200 |
0.7170 |
0.3015 |
3.4542 |
|
EUCALYPTUS OIL CAP X 15 |
EUCALYPTUS OIL CAP X 60 |
0.1140 |
0.4029 |
1.8312 |
0.2200 |
0.7170 |
0.2551 |
3.1530 |
|
EUCALYPTUS OIL CAP X 15 |
SRH 30 MASSAGE RUBING OIL |
0.1097 |
0.3876 |
1.2879 |
0.2830 |
0.6990 |
0.1208 |
4.2884 |
|
SRH 30 MASSAGE RUBING OIL |
EUCALYPTUS OIL CAP X 15 |
0.1097 |
0.3644 |
1.2879 |
0.2830 |
0.6990 |
0.1165 |
4.2816 |
|
SRH 30 MASSAGE RUBING OIL |
EUCALYPTUS OIL CAP X 30 |
0.1091 |
0.3624 |
1.1836 |
0.3010 |
0.6938 |
0.0804 |
2.6053 |
|
EUCALYPTUS OIL CAP X 30 |
SRH 30 MASSAGE RUBING OIL |
0.1091 |
0.3562 |
1.1836 |
0.3010 |
0.6938 |
0.0796 |
3.6793 |
|
MUSCLE BALM 10 |
SRH 30 MASSAGE RUBING OIL |
0.1050 |
0.5254 |
1.7454 |
0.1999 |
0.6990 |
0.2804 |
2.5473 |
|
SRH 30 MASSAGE RUBING OIL |
MUSCLE BALM 10 |
0.1050 |
0.3489 |
1.7454 |
0.1999 |
0.6990 |
0.2132 |
3.7167 |
|
SRH 60 MASSAGE RUBING OIL |
MUSCLE BALM 20 |
0.0960 |
0.4449 |
1.9209 |
0.2158 |
0.7684 |
0.2720 |
3.2586 |
|
MUSCLE BALM 20 |
SRH 60 MASSAGE RUBING OIL |
0.0960 |
0.4146 |
1.9209 |
0.2158 |
0.7684 |
0.2586 |
4.8991 |
|
WHITEWOOD OIL CAP X 15, EUCALYPTUS
OIL CAP X 60 |
EUCALYPTUS OIL CAP X 30 |
0.0937 |
0.8222 |
2.6853 |
0.1140 |
0.6938 |
0.5824 |
3.4965 |
|
WHITEWOOD OIL CAP X 60, EUCALYPTUS
OIL CAP X 30 |
EUCALYPTUS OIL CAP X 15 |
0.0937 |
0.6894 |
2.4365 |
0.1360 |
0.7170 |
0.4704 |
4.7302 |
|
WHITEWOOD OIL CAP X 15, EUCALYPTUS
OIL CAP X 30 |
EUCALYPTUS OIL CAP X 60 |
0.0937 |
0.5146 |
2.3387 |
0.1822 |
0.7799 |
0.3602 |
2.8936 |
|
EUCALYPTUS OIL CAP X 60 |
WHITEWOOD OIL CAP X 15, EUCALYPTUS
OIL CAP X 30 |
0.0937 |
0.4260 |
2.3387 |
0.1822 |
0.7799 |
0.3127 |
3.0536 |
|
EUCALYPTUS OIL CAP X 30 |
WHITEWOOD OIL CAP X 15, EUCALYPTUS
OIL CAP X 60 |
0.0937 |
0.3062 |
2.6853 |
0.1140 |
0.6938 |
0.2770 |
3.5603 |
|
EUCALYPTUS OIL CAP X 15 |
WHITEWOOD OIL CAP X 60, EUCALYPTUS
OIL CAP X 30 |
0.0937 |
0.3313 |
2.4365 |
0.1360 |
0.7170 |
0.2724 |
3.0178 |
|
BASLEM 20 |
MUSCLE BALM 20 |
0.0923 |
0.6243 |
2.6954 |
0.1478 |
0.7684 |
0.4608 |
3.3855 |
|
MUSCLE BALM 20 |
BASLEM 20 |
0.0923 |
0.3983 |
2.6954 |
0.1478 |
0.7684 |
0.3261 |
2.7565 |
|
BASLEM 10 |
MUSCLE BALM 10 |
0.0904 |
0.6337 |
3.1696 |
0.1427 |
0.8001 |
0.5059 |
3.1486 |
|
MUSCLE BALM 10 |
BASLEM 10 |
0.0904 |
0.4522 |
3.1696 |
0.1427 |
0.8001 |
0.3869 |
3.9226 |
|
EUCALYPTUS OIL CAP X 60 |
SRH 30 MASSAGE RUBING OIL |
0.0830 |
0.3772 |
1.2531 |
0.2200 |
0.6990 |
0.0977 |
2.5528 |
|
SRH 30 MASSAGE RUBING OIL |
EUCALYPTUS OIL CAP X 60 |
0.0830 |
0.2757 |
1.2531 |
0.2200 |
0.6990 |
0.0797 |
2.9077 |
|
SRH 30 RUBBING OIL, EUCALYPTUS
OIL CAP X 30 |
EUCALYPTUS OIL CAP X 15 |
0.0823 |
0.7549 |
2.6679 |
0.1091 |
0.7170 |
0.5297 |
3.7072 |
|
WHITEWOOD OIL CAP X 15,
SRH 30 RUBBING OIL |
EUCALYPTUS OIL CAP X 30 |
0.0823 |
0.7507 |
2.4518 |
0.1097 |
0.6938 |
0.4993 |
2.6581 |
|
EUCALYPTUS OIL CAP X 15 |
SRH 30 RUBBING OIL, EUCALYPTUS
OIL CAP X 30 |
0.0823 |
0.2910 |
2.6679 |
0.1091 |
0.7170 |
0.2537 |
3.8372 |
|
EUCALYPTUS OIL CAP X 30 |
WHITEWOOD OIL CAP X 15,
SRH 30 RUBBING OIL |
0.0823 |
0.2689 |
2.4518 |
0.1097 |
0.6938 |
0.2295 |
2.8314 |
|
WHITEWOOD OIL CAP X 15, EUCALYPTUS
OIL CAP X 30 |
SRH 30 MASSAGE RUBING OIL |
0.0823 |
0.4520 |
1.5017 |
0.1822 |
0.6990 |
0.1847 |
2.8483 |
|
SRH 30 MASSAGE RUBING OIL |
WHITEWOOD OIL CAP X 15, EUCALYPTUS
OIL CAP X 30 |
0.0823 |
0.2736 |
1.5017 |
0.1822 |
0.6990 |
0.1308 |
2.7171 |
|
MUSCLE BALM 10 |
SRH 60 MASSAGE RUBING OIL |
0.0809 |
0.4046 |
1.8748 |
0.1999 |
0.7842 |
0.2360 |
2.4704 |
|
SRH 60 MASSAGE RUBING OIL |
MUSCLE BALM 10 |
0.0809 |
0.3748 |
1.8748 |
0.1999 |
0.7842 |
0.2230 |
2.3823 |
|
BASLEM 20 |
SRH 30 MASSAGE RUBING OIL |
0.0797 |
0.5395 |
1.7924 |
0.1478 |
0.6990 |
0.2799 |
3.1766 |
|
SRH 30 MASSAGE RUBING OIL |
BASLEM 20 |
0.0797 |
0.2649 |
1.7924 |
0.1478 |
0.6990 |
0.1675 |
2.3051 |
|
BASLEM 10 |
SRH 30 MASSAGE RUBING OIL |
0.0781 |
0.5470 |
1.8174 |
0.1427 |
0.6990 |
0.2870 |
2.5913 |
|
SRH 30 MASSAGE RUBING OIL |
BASLEM 10 |
0.0781 |
0.2593 |
1.8174 |
0.1427 |
0.6990 |
0.1669 |
2.2829 |
|
MUSCLE BALM 20 |
EUCALYPTUS OIL CAP X 30 |
0.0758 |
0.3274 |
1.0692 |
0.2316 |
0.6938 |
0.0276 |
2.9954 |
Then the following is a table of results from
implementing the FP-GROWTH algorithm for the top to lowest rules based on the
Confidence value.
Table
14. FP-Grworth Results: Rules based on Confidence values
|
antecedents |
consequences |
Supp |
Conf |
Elevator |
Bi-Support |
�Bi-Supp |
Bi-Confidence |
Bi-Lift |
|
WHITEWOOD OIL CAP X 15, EUCALYPTUS
OIL CAP X 60 |
EUCALYPTUS OIL CAP X 30 |
0.0937 |
0.8222 |
2.6853 |
0.1140 |
0.6938 |
0.5824 |
6.0854 |
|
SRH 30 RUBBING OIL, EUCALYPTUS
OIL CAP X 30 |
EUCALYPTUS OIL CAP X 15 |
0.0823 |
0.7549 |
2.6679 |
0.1091 |
0.7170 |
0.5297 |
6.5737 |
|
WHITEWOOD OIL CAP X 15,
SRH 30 RUBBING OIL |
EUCALYPTUS OIL CAP X 30 |
0.0823 |
0.7507 |
2.4518 |
0.1097 |
0.6938 |
0.4993 |
5.6075 |
|
WHITEWOOD OIL CAP X 60, EUCALYPTUS
OIL CAP X 30 |
EUCALYPTUS OIL CAP X 15 |
0.0937 |
0.6894 |
2.4365 |
0.1360 |
0.7170 |
0.4704 |
6.3255 |
|
EUCALYPTUS OIL CAP X 15 |
EUCALYPTUS OIL CAP X 30 |
0.1822 |
0.6438 |
2.1026 |
0.2830 |
0.6938 |
0.4708 |
2.4520 |
|
BASLEM 10 |
MUSCLE BALM 10 |
0.0904 |
0.6337 |
3.1696 |
0.1427 |
0.8001 |
0.5059 |
5.2734 |
|
SRH 60 MASSAGE RUBING OIL |
SRH 30 MASSAGE RUBING OIL |
0.1361 |
0.6308 |
2.0958 |
0.2158 |
0.6990 |
0.4206 |
3.8435 |
|
BASLEM 20 |
MUSCLE BALM 20 |
0.0923 |
0.6243 |
2.6954 |
0.1478 |
0.7684 |
0.4608 |
5.1996 |
|
EUCALYPTUS OIL CAP X 60 |
EUCALYPTUS OIL CAP X 30 |
0.1360 |
0.6179 |
2.0182 |
0.2200 |
0.6938 |
0.3997 |
2.3418 |
|
MUSCLE BALM 10 |
MUSCLE BALM 20 |
0.1206 |
0.6034 |
2.6048 |
0.1999 |
0.7684 |
0.4646 |
3.2389 |
|
EUCALYPTUS OIL CAP X 30 |
EUCALYPTUS OIL CAP X 15 |
0.1822 |
0.5949 |
2.1026 |
0.2830 |
0.6938 |
0.4497 |
3.4542 |
|
BASLEM 10 |
SRH 30 MASSAGE RUBING OIL |
0.0781 |
0.5470 |
1.8174 |
0.1427 |
0.6990 |
0.2870 |
3.1530 |
|
BASLEM 20 |
SRH 30 MASSAGE RUBING OIL |
0.0797 |
0.5395 |
1.7924 |
0.1478 |
0.6990 |
0.2799 |
4.2884 |
|
MUSCLE BALM 10 |
SRH 30 MASSAGE RUBING OIL |
0.1050 |
0.5254 |
1.7454 |
0.1999 |
0.6990 |
0.2804 |
4.2816 |
|
MUSCLE BALM 20 |
MUSCLE BALM 10 |
0.1206 |
0.5207 |
2.6048 |
0.1999 |
0.7684 |
0.4175 |
2.6053 |
|
EUCALYPTUS OIL CAP X 60 |
EUCALYPTUS OIL CAP X 15 |
0.1140 |
0.5181 |
1.8312 |
0.2200 |
0.7170 |
0.3015 |
3.6793 |
|
WHITEWOOD OIL CAP X 15, EUCALYPTUS
OIL CAP X 30 |
EUCALYPTUS OIL CAP X 60 |
0.0937 |
0.5146 |
2.3387 |
0.1822 |
0.7799 |
0.3602 |
2.5473 |
|
MUSCLE BALM 20 |
SRH 30 MASSAGE RUBING OIL |
0.1181 |
0.5098 |
1.6938 |
0.2316 |
0.6990 |
0.2718 |
3.7167 |
|
SRH 30 MASSAGE RUBING OIL |
SRH 60 MASSAGE RUBING OIL |
0.1361 |
0.4523 |
2.0958 |
0.2158 |
0.6990 |
0.3383 |
3.2586 |
|
MUSCLE BALM 10 |
BASLEM 10 |
0.0904 |
0.4522 |
3.1696 |
0.1427 |
0.8001 |
0.3869 |
4.8991 |
|
WHITEWOOD OIL CAP X 15, EUCALYPTUS
OIL CAP X 30 |
SRH 30 MASSAGE RUBING OIL |
0.0823 |
0.4520 |
1.5017 |
0.1822 |
0.6990 |
0.1847 |
3.4965 |
|
SRH 60 MASSAGE RUBING OIL |
MUSCLE BALM 20 |
0.0960 |
0.4449 |
1.9209 |
0.2158 |
0.7684 |
0.2720 |
4.7302 |
|
EUCALYPTUS OIL CAP X 30 |
EUCALYPTUS OIL CAP X 60 |
0.1360 |
0.4441 |
2.0182 |
0.2200 |
0.6938 |
0.3229 |
2.8936 |
|
EUCALYPTUS OIL CAP X 60 |
WHITEWOOD OIL CAP X 15, EUCALYPTUS
OIL CAP X 30 |
0.0937 |
0.4260 |
2.3387 |
0.1822 |
0.7799 |
0.3127 |
3.0536 |
|
MUSCLE BALM 20 |
SRH 60 MASSAGE RUBING OIL |
0.0960 |
0.4146 |
1.9209 |
0.2158 |
0.7684 |
0.2586 |
3.5603 |
|
MUSCLE BALM 10 |
SRH 60 MASSAGE RUBING OIL |
0.0809 |
0.4046 |
1.8748 |
0.1999 |
0.7842 |
0.2360 |
3.0178 |
|
EUCALYPTUS OIL CAP X 15 |
EUCALYPTUS OIL CAP X 60 |
0.1140 |
0.4029 |
1.8312 |
0.2200 |
0.7170 |
0.2551 |
3.3855 |
|
MUSCLE BALM 20 |
BASLEM 20 |
0.0923 |
0.3983 |
2.6954 |
0.1478 |
0.7684 |
0.3261 |
2.7565 |
|
SRH 30 MASSAGE RUBING OIL |
MUSCLE BALM 20 |
0.1181 |
0.3923 |
1.6938 |
0.2316 |
0.6990 |
0.2299 |
3.1486 |
|
EUCALYPTUS OIL CAP X 15 |
SRH 30 MASSAGE RUBING OIL |
0.1097 |
0.3876 |
1.2879 |
0.2830 |
0.6990 |
0.1208 |
3.9226 |
|
EUCALYPTUS OIL CAP X 60 |
SRH 30 MASSAGE RUBING OIL |
0.0830 |
0.3772 |
1.2531 |
0.2200 |
0.6990 |
0.0977 |
2.5528 |
|
SRH 60 MASSAGE RUBING OIL |
MUSCLE BALM 10 |
0.0809 |
0.3748 |
1.8748 |
0.1999 |
0.7842 |
0.2230 |
2.9077 |
|
SRH 30 MASSAGE RUBING OIL |
EUCALYPTUS OIL CAP X 15 |
0.1097 |
0.3644 |
1.2879 |
0.2830 |
0.6990 |
0.1165 |
3.7072 |
|
SRH 30 MASSAGE RUBING OIL |
EUCALYPTUS OIL CAP X 30 |
0.1091 |
0.3624 |
1.1836 |
0.3010 |
0.6938 |
0.0804 |
2.6581 |
|
EUCALYPTUS OIL CAP X 30 |
SRH 30 MASSAGE RUBING OIL |
0.1091 |
0.3562 |
1.1836 |
0.3010 |
0.6938 |
0.0796 |
3.8372 |
|
SRH 30 MASSAGE RUBING OIL |
MUSCLE BALM 10 |
0.1050 |
0.3489 |
1.7454 |
0.1999 |
0.6990 |
0.2132 |
2.8314 |
|
EUCALYPTUS OIL CAP X 15 |
WHITEWOOD OIL CAP X 60, EUCALYPTUS
OIL CAP X 30 |
0.0937 |
0.3313 |
2.4365 |
0.1360 |
0.7170 |
0.2724 |
2.8483 |
|
MUSCLE BALM 20 |
EUCALYPTUS OIL CAP X 30 |
0.0758 |
0.3274 |
1.0692 |
0.2316 |
0.6938 |
0.0276 |
2.7171 |
|
EUCALYPTUS OIL CAP X 30 |
WHITEWOOD OIL CAP X 15, EUCALYPTUS
OIL CAP X 60 |
0.0937 |
0.3062 |
2.6853 |
0.1140 |
0.6938 |
0.2770 |
2.4704 |
|
EUCALYPTUS OIL CAP X 15 |
SRH 30 RUBBING OIL, EUCALYPTUS
OIL CAP X 30 |
0.0823 |
0.2910 |
2.6679 |
0.1091 |
0.7170 |
0.2537 |
2.3823 |
|
SRH 30 MASSAGE RUBING OIL |
EUCALYPTUS OIL CAP X 60 |
0.0830 |
0.2757 |
1.2531 |
0.2200 |
0.6990 |
0.0797 |
3.1766 |
|
SRH 30 MASSAGE RUBING OIL |
WHITEWOOD OIL CAP X 15, EUCALYPTUS
OIL CAP X 30 |
0.0823 |
0.2736 |
1.5017 |
0.1822 |
0.6990 |
0.1308 |
2.3051 |
|
EUCALYPTUS OIL CAP X 30 |
WHITEWOOD OIL CAP X 15,
SRH 30 RUBBING OIL |
0.0823 |
0.2689 |
2.4518 |
0.1097 |
0.6938 |
0.2295 |
2.5913 |
|
SRH 30 MASSAGE RUBING OIL |
BASLEM 20 |
0.0797 |
0.2649 |
1.7924 |
0.1478 |
0.6990 |
0.1675 |
2.2829 |
|
SRH 30 MASSAGE RUBING OIL |
BASLEM 10 |
0.0781 |
0.2593 |
1.8174 |
0.1427 |
0.6990 |
0.1669 |
2.9954 |
Then the following is a table of results from
implementing the FP-GROWTH algorithm for the top 15 rules based on the lift
value.
Table
15. FP-Grworth Results: Rules based on Lift values
|
antecedents |
consequences |
Supp |
Conf |
Elevator |
Bi-Support |
�Bi-Supp |
Bi-Confidence |
Bi-Lift |
|
BASLEM 10 |
MUSCLE BALM 10 |
0.0904 |
0.6337 |
3.1696 |
0.1427 |
0.8001 |
0.5059 |
6.0854 |
|
MUSCLE BALM 10 |
BASLEM 10 |
0.0904 |
0.4522 |
3.1696 |
0.1427 |
0.8001 |
0.3869 |
6.5737 |
|
BASLEM 20 |
MUSCLE BALM 20 |
0.0923 |
0.6243 |
2.6954 |
0.1478 |
0.7684 |
0.4608 |
5.6075 |
|
MUSCLE BALM 20 |
BASLEM 20 |
0.0923 |
0.3983 |
2.6954 |
0.1478 |
0.7684 |
0.3261 |
6.3255 |
|
WHITEWOOD OIL CAP X 15, EUCALYPTUS
OIL CAP X 60 |
EUCALYPTUS OIL CAP X 30 |
0.0937 |
0.8222 |
2.6853 |
0.1140 |
0.6938 |
0.5824 |
2.4520 |
|
EUCALYPTUS OIL CAP X 30 |
WHITEWOOD OIL CAP X 15, EUCALYPTUS
OIL CAP X 60 |
0.0937 |
0.3062 |
2.6853 |
0.1140 |
0.6938 |
0.2770 |
5.2734 |
|
SRH 30 RUBBING OIL, EUCALYPTUS
OIL CAP X 30 |
EUCALYPTUS OIL CAP X 15 |
0.0823 |
0.7549 |
2.6679 |
0.1091 |
0.7170 |
0.5297 |
3.8435 |
|
EUCALYPTUS OIL CAP X 15 |
SRH 30 RUBBING OIL, EUCALYPTUS
OIL CAP X 30 |
0.0823 |
0.2910 |
2.6679 |
0.1091 |
0.7170 |
0.2537 |
5.1996 |
|
MUSCLE BALM 10 |
MUSCLE BALM 20 |
0.1206 |
0.6034 |
2.6048 |
0.1999 |
0.7684 |
0.4646 |
2.3418 |
|
MUSCLE BALM 20 |
MUSCLE BALM 10 |
0.1206 |
0.5207 |
2.6048 |
0.1999 |
0.7684 |
0.4175 |
3.2389 |
|
WHITEWOOD OIL CAP X 15,
SRH 30 RUBBING OIL |
EUCALYPTUS OIL CAP X 30 |
0.0823 |
0.7507 |
2.4518 |
0.1097 |
0.6938 |
0.4993 |
3.4542 |
|
EUCALYPTUS OIL CAP X 30 |
WHITEWOOD OIL CAP X 15,
SRH 30 RUBBING OIL |
0.0823 |
0.2689 |
2.4518 |
0.1097 |
0.6938 |
0.2295 |
3.1530 |
|
WHITEWOOD OIL CAP X 60, EUCALYPTUS
OIL CAP X 30 |
EUCALYPTUS OIL CAP X 15 |
0.0937 |
0.6894 |
2.4365 |
0.1360 |
0.7170 |
0.4704 |
4.2884 |
|
EUCALYPTUS OIL CAP X 15 |
WHITEWOOD OIL CAP X 60, EUCALYPTUS
OIL CAP X 30 |
0.0937 |
0.3313 |
2.4365 |
0.1360 |
0.7170 |
0.2724 |
4.2816 |
|
WHITEWOOD OIL CAP X 15, EUCALYPTUS
OIL CAP X 30 |
EUCALYPTUS OIL CAP X 60 |
0.0937 |
0.5146 |
2.3387 |
0.1822 |
0.7799 |
0.3602 |
2.6053 |
|
EUCALYPTUS OIL CAP X 60 |
WHITEWOOD OIL CAP X 15, EUCALYPTUS
OIL CAP X 30 |
0.0937 |
0.4260 |
2.3387 |
0.1822 |
0.7799 |
0.3127 |
3.6793 |
|
EUCALYPTUS OIL CAP X 15 |
EUCALYPTUS OIL CAP X 30 |
0.1822 |
0.6438 |
2.1026 |
0.2830 |
0.6938 |
0.4708 |
2.5473 |
|
EUCALYPTUS OIL CAP X 30 |
EUCALYPTUS OIL CAP X 15 |
0.1822 |
0.5949 |
2.1026 |
0.2830 |
0.6938 |
0.4497 |
3.7167 |
|
SRH 60 MASSAGE RUBING OIL |
SRH 30 MASSAGE RUBING OIL |
0.1361 |
0.6308 |
2.0958 |
0.2158 |
0.6990 |
0.4206 |
3.2586 |
|
SRH 30 MASSAGE RUBING OIL |
SRH 60 MASSAGE RUBING OIL |
0.1361 |
0.4523 |
2.0958 |
0.2158 |
0.6990 |
0.3383 |
4.8991 |
|
EUCALYPTUS OIL CAP X 60 |
EUCALYPTUS OIL CAP X 30 |
0.1360 |
0.6179 |
2.0182 |
0.2200 |
0.6938 |
0.3997 |
3.4965 |
|
EUCALYPTUS OIL CAP X 30 |
EUCALYPTUS OIL CAP X 60 |
0.1360 |
0.4441 |
2.0182 |
0.2200 |
0.6938 |
0.3229 |
4.7302 |
|
SRH 60 MASSAGE RUBING OIL |
MUSCLE BALM 20 |
0.0960 |
0.4449 |
1.9209 |
0.2158 |
0.7684 |
0.2720 |
2.8936 |
|
MUSCLE BALM 20 |
SRH 60 MASSAGE RUBING OIL |
0.0960 |
0.4146 |
1.9209 |
0.2158 |
0.7684 |
0.2586 |
3.0536 |
|
MUSCLE BALM 10 |
SRH 60 MASSAGE RUBING OIL |
0.0809 |
0.4046 |
1.8748 |
0.1999 |
0.7842 |
0.2360 |
3.5603 |
|
SRH 60 MASSAGE RUBING OIL |
MUSCLE BALM 10 |
0.0809 |
0.3748 |
1.8748 |
0.1999 |
0.7842 |
0.2230 |
3.0178 |
|
EUCALYPTUS OIL CAP X 60 |
EUCALYPTUS OIL CAP X 15 |
0.1140 |
0.5181 |
1.8312 |
0.2200 |
0.7170 |
0.3015 |
3.3855 |
|
EUCALYPTUS OIL CAP X 15 |
EUCALYPTUS OIL CAP X 60 |
0.1140 |
0.4029 |
1.8312 |
0.2200 |
0.7170 |
0.2551 |
2.7565 |
|
BASLEM 10 |
SRH 30 MASSAGE RUBING OIL |
0.0781 |
0.5470 |
1.8174 |
0.1427 |
0.6990 |
0.2870 |
3.1486 |
|
SRH 30 MASSAGE RUBING OIL |
BASLEM 10 |
0.0781 |
0.2593 |
1.8174 |
0.1427 |
0.6990 |
0.1669 |
3.9226 |
|
BASLEM 20 |
SRH 30 MASSAGE RUBING OIL |
0.0797 |
0.5395 |
1.7924 |
0.1478 |
0.6990 |
0.2799 |
2.5528 |
|
SRH 30 MASSAGE RUBING OIL |
BASLEM 20 |
0.0797 |
0.2649 |
1.7924 |
0.1478 |
0.6990 |
0.1675 |
2.9077 |
|
MUSCLE BALM 10 |
SRH 30 MASSAGE RUBING OIL |
0.1050 |
0.5254 |
1.7454 |
0.1999 |
0.6990 |
0.2804 |
3.7072 |
|
SRH 30 MASSAGE RUBING OIL |
MUSCLE BALM 10 |
0.1050 |
0.3489 |
1.7454 |
0.1999 |
0.6990 |
0.2132 |
2.6581 |
|
MUSCLE BALM 20 |
SRH 30 MASSAGE RUBING OIL |
0.1181 |
0.5098 |
1.6938 |
0.2316 |
0.6990 |
0.2718 |
3.8372 |
|
SRH 30 MASSAGE RUBING OIL |
MUSCLE BALM 20 |
0.1181 |
0.3923 |
1.6938 |
0.2316 |
0.6990 |
0.2299 |
2.8314 |
|
WHITEWOOD OIL CAP X 15, EUCALYPTUS
OIL CAP X 30 |
SRH 30 MASSAGE RUBING OIL |
0.0823 |
0.4520 |
1.5017 |
0.1822 |
0.6990 |
0.1847 |
2.8483 |
|
SRH 30 MASSAGE RUBING OIL |
WHITEWOOD OIL CAP X 15, EUCALYPTUS
OIL CAP X 30 |
0.0823 |
0.2736 |
1.5017 |
0.1822 |
0.6990 |
0.1308 |
2.7171 |
|
EUCALYPTUS OIL CAP X 15 |
SRH 30 MASSAGE RUBING OIL |
0.1097 |
0.3876 |
1.2879 |
0.2830 |
0.6990 |
0.1208 |
2.4704 |
|
SRH 30 MASSAGE RUBING OIL |
EUCALYPTUS OIL CAP X 15 |
0.1097 |
0.3644 |
1.2879 |
0.2830 |
0.6990 |
0.1165 |
2.3823 |
|
EUCALYPTUS OIL CAP X 60 |
SRH 30 MASSAGE RUBING OIL |
0.0830 |
0.3772 |
1.2531 |
0.2200 |
0.6990 |
0.0977 |
3.1766 |
|
SRH 30 MASSAGE RUBING OIL |
EUCALYPTUS OIL CAP X 60 |
0.0830 |
0.2757 |
1.2531 |
0.2200 |
0.6990 |
0.0797 |
2.3051 |
|
SRH 30 MASSAGE RUBING OIL |
EUCALYPTUS OIL CAP X 30 |
0.1091 |
0.3624 |
1.1836 |
0.3010 |
0.6938 |
0.0804 |
2.5913 |
|
EUCALYPTUS OIL CAP X 30 |
SRH 30 MASSAGE RUBING OIL |
0.1091 |
0.3562 |
1.1836 |
0.3010 |
0.6938 |
0.0796 |
2.2829 |
|
MUSCLE BALM 20 |
EUCALYPTUS OIL CAP X 30 |
0.0758 |
0.3274 |
1.0692 |
0.2316 |
0.6938 |
0.0276 |
2.9954 |
Analysis of the results of the rules
From the results of the existing rules, several
things can be analyzed:
Table
16. Analysis of the results of the rules
|
Analysis |
Results |
|
Some combinations often appear in
transactions and have a high level of trust. |
Almost half of the top 10 rules confidence
is also in the top 10 rules support. For example, the rule "CAP EUCALYPTUS
OIL X 15 → CAP EUCALYPTUS OIL X 30" with a support value of 18.22%
and a confidence of 64.38%. |
|
There is a correlation between the level of
confidence (support) and the increase (lift) of the product. |
Half of the top 10 rules confidence also
appears in the top 10 rules lift. For example, in the rule "CAP EUCALYPTUS
OIL X 15, CAP EUCALYPTUS OIL X 60 → CAP EUCALYPTUS OIL X 30" with
a confidence value of 82.22% and a lift of 2.68. |
|
It was found that the relationship between
products in terms of lift was also supported by a high confidence level. |
Five rules from the top 10 rules lift the
top 10 rules confidence. |
|
It was found that although these rules have
a significant lift, the frequency of occurrence of the combinations of
itemset A and B in the dataset is relatively low. |
2 rules from the top 10 rules support are
in the top 10 rules lift |
|
The concept analysis of bi-support combines
the two rules in two directions to consider the frequency of occurrence of
itemset A and B combinations. |
There is a difference between the value of
support and bi-support, where the value tends to be higher than the value of
support. |
|
Conceptual analysis of confidence only
considers itemset B's appearance when itemset A occurs, while bi-confidence
involves the correlation between itemset A and B. |
The difference between the confidence value
and the bi-confidence value. The bi-confidence value tends to be lower than
the confidence value. |
|
Concept analysis of Bi-lift is to consider
the relationship between itemsets A and B regardless of whether itemset A
occurs or not. |
The difference in the value of the lift
with the bi-lift. Bi-lift values tend to be higher than lift
values. |
Overall, this
analysis shows a consistent buying pattern between the products on the list.
This template can be used to optimize marketing and sales strategies, such as
grouping frequently purchased products together or promoting cross-products to
drive higher sales. This can be a reference for stores or sellers to carry out
more effective sales strategies, such as placing products often purchased
together close to each other in the store.
CONCLUSION
In conclusion, the analysis of
sales data at PT. XYZ from July 2022 to December 2022, using the Apriori and
FP-GROWTH association rules mining algorithms on 100,497 transactions, reveals
several key findings: 1) Apriori is the preferred choice when additional
measures like Bi-support, Bi-confidence, and Bi-Lift are not required, as it
outperforms FP-Growth in terms of time and memory efficiency. 2) Apriori is
ideal for practicality and memory conservation, offering a straightforward
implementation with lower memory usage (approximately 482.32 MiB) compared to
FP-GROWTH. 3) FP-GROWTH excels in execution time, being nearly half as fast as
Apriori (84,655 seconds compared to 168,488 seconds), even with frequent
parameter adjustments. 4) Both Apriori and FP-GROWTH algorithms yield identical
results in terms of generated rules, including antecedents, consequents,
support, confidence, lift, Bi-support, Bi-confidence, and Bi-lift. 5) The use
of Bi-support confirms that support values in both algorithms are equal to or
greater than Bi-Support values, validating support measurements. 6) Introducing
Bi-support, Bi-Confidence, and Bi-Lift calculations provides deeper insights
into itemset relationships within the dataset, enhancing association rule
analysis. 7) OVAR calculations yield similar results in both algorithms, with
minor discrepancies possibly arising from differences in implementation or
calculations. 8) FP-GROWTH is more effective for datasets with a large and
diverse number of itemsets, while Apriori excels in ease of implementation for
smaller datasets. Algorithm choice should align with dataset characteristics
and project requirements. In summary, PT. XYZ's association rules reveal the
highest combination value within a seventy-six itemset. Identifying these rules
can enhance business efficiency, predict consumer behavior, and boost company
profitability. The choice of the appropriate algorithm should be based on the
dataset's specifics and project objectives.
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2023 by the authors. It was submitted for possible open-access publication
under the terms and conditions of the Creative Commons Attribution (CC BY SA) license (https://creativecommons.org/licenses/by
-sa / 4 .0/ ). |