KEDS_Paper_Template


Knowledge Engineering and Data Science (KEDS) pISSN 2597-4602 

Vol 3, No 2, December 2020, pp. 89–98 eISSN 2597-4637 
 

 

 

 

https://doi.org/10.17977/um018v3i22020p89-98   
©2020 Knowledge Engineering and Data Science | W : http://journal2.um.ac.id/index.php/keds  | E : keds.journal@um.ac.id 

This is an open access article under the CC BY-SA license (https://creativecommons.org/licenses/by-sa/4.0/) 

Simple Modification for an Apriori Algorithm with 

Combination Reduction and Iteration Limitation Technique 

Adie Wahyudi Oktavia Gama a, 1, *, Ni Made Widnyani b, 2 

a Department of Information Technology, Universitas Pendidikan Nasional 

Bedugul St Number 39, Denpasar, 80224, Indonesia 

b Department of Digital Business, Universitas Bali Internasional 

Seroja St Jeruk Block Number 9, Denpasar, 80239, Indonesia 

1 gama.adiewahyudi@gmail.com *; 2 nimadewidnyani90@gmail.com 

* corresponding author 

 

 

I. Introduction 

Management information systems or systems that are related to transactions will produce data that 
continue to grow every time a process is carried out. Those data that continue to grow are not balanced 
with the acquisition of information for other decision support. The information produced is usually 
monotonous in the form of daily, weekly or annual reports. This phenomenon is often referred to as 
"rich information poor data", which means an increase in the amount of data is not comparable to the 
information obtained. This is due to a lack of analysis of the data stack. 

Data mining is the solution to this phenomenon. Data mining is a method that applies data analysis 
and algorithms to create a specific identification of designs or model over the data [1]. Data mining 
will analyse large data to extract valuable new information or knowledge. One of data mining 
techniques that can be used to explore the relationship of new knowledge in the form of a combination 
of items hidden in the database is association analysis. The relationship can be represented in the form 
of association rules [2][3]. Association analysis measures the relationship between two or more hidden 
items in the database. The form of association rules is "If Antecedent Then Consequent", which means 
that the relationship strength of a product is determined when it is purchased with other products. The 
strength of the relationship of an associative rule can be measured by two parameters called support 
and confidence. Support value is the percentage of occurrence of a combination of items in the 
database. Confidence value or value of certainty reflects the strength of the relationships between 
items forming combinations in associative rules formed by the association. 

 

ARTICLE INFO A B S T R A C T   

Article history: 

Received 19 September 2020 

Revised 13 October 2020 

Accepted 03 November 2020 

Published online 31 December 2020 

 

Apriori algorithm is one of the methods with regard to association rules in data mining. 
This algorithm uses knowledge from an itemset previously formed with frequent 
occurrence frequencies to form the next itemset. An a priori algorithm generates a 
combination by iteration methods that are using repeated database scanning process, 
pairing one product with another product and then recording the number of 
occurrences of the combination with the minimum limit of support and confidence 
values. The a priori algorithm will slow down to an expanding database in the process 
of finding frequent itemset to form association rules. Modification techniques are 
needed to optimize the performance of a priori algorithms so as to get frequent itemset 
and to form association rules in a short time. Modifications in this study are obtained 
by using techniques combination reduction and iteration limitation. Testing is done by 
comparing the time and quality of the rules formed from the database scanning using 
a priori algorithms with and without modification. The results of the test show that the 
modified a priori algorithm tested with data samples of up to 500 transactions is 
proven to form rules faster with quality rules that are maintained. 

This is an open access article under the CC BY-SA license 

(https://creativecommons.org/licenses/by-sa/4.0/). 

Keywords: 

Data Mining 

Association Rules 

Apriori Algorithm 

Frequent Itemset 

Apriori Optimization 

http://u.lipi.go.id/1502081730
http://u.lipi.go.id/1502081046
https://doi.org/10.17977/um018v3i22020p89-98
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mailto:keds.journal@um.ac.id
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90 A.W.O. Gama and N.M. Widnyani / Knowledge Engineering and Data Science 2020, 3 (2): 89–98 

Apriori algorithm is one of the forming rules of association in data mining. The initial research 
conducted by Agrawal in 1993 with the title "Mining Association Rules Between Sets of Items in 
Large Databases" was the beginning of the development of association methods using apriori 
algorithms [4]. In 1994, Agrawal and Srikant again conducted research with the association method 
entitled "Fast Algorithms for Mining Association Rules" [5]. The research was then focused on 
refining apriori algorithms that had been developed previously and from there apriori algorithm was 
known as one of the association rules forming algorithms. Apriori algorithm takes an iterative 
approach, that is, generating k-itemset that is used to form the next (k + 1) -itemset. The principle of 
apriori algorithm is if an itemset often appears frequently, then all subsets of the itemset must also 
appear frequently in all transactions stored in a database [2]. 

In this algorithm candidate (k + 1) -itemset is generated by combining two itemset on domain / 
size k. Candidates of (k + 1) -itemset containing the frequency of subset that rarely appears or below 
the threshold will be trimmed and not used in determining association rules [2]. In accordance with 
association rules, apriori algorithms also use minimum support and minimum confidence to determine 
itemset rules which are suitable for use in decision making. 

1-itemset is used to find 2-itemset, which is a combination of 2 items, for example, if buy Shirt 
then buy Long Pants. 2-itemset is then used to find 3-itemset which is a combination of 3 items, for 
example if you buy Shirts and buy pens then buy Long Pants and so on until there are no more k-
itemset that can be found in the database transaction [6]. 

Apriori reasoning uses prior knowledge of an itemset with frequent occurrence frequencies. It uses 
an iterative approach where k-itemset is used to explore (k + 1)-itemset [6]. Candidate (k + 1) -itemset 
is generated from merging two itemset on domain k. Candidate (k + 1) -itemset containing the 
frequency of subset that rarely appears or below the threshold will be trimmed and not used to form 
association rules [2]. 

There is a relatively huge amount of research on apriori algorithms [7][8][9][10][11][12][13]. 
Studies related to the application of apriori algorithms that are used as references in this study are as 
follows: 

1. The application of apriori algorithms that had been previously developed without using 
optimization techniques to obtain more efficient association rules [14].  

2. Improvised the apriori algorithm by determining "set size" and "set size frequency". Set size is 
the number of items per transaction while the set size frequency is the number of transactions 
that have at least "set size" items. This set size and set size frequency are used to eliminate 
insignificant key candidates [15]. 

3. Optimization of apriori algorithms by reducing or pruning the number of candidates for frequent 
itemset candidates on itemset Ck [16]. 

4. Improvised the apriori algorithm by reducing the number of transactions (transaction reduction) 
whose number of items transaction did not meet the specified limit value. Reducing these 
transactions has an impact on efficiency improvement when scanning databases [17]. 

5. The utilization of apriori algorithms to establish customer segmentation in the SMES sector 
[18]. 

6. Application of apriori algorithms to form associations in sales database [19][20][21]. 

The essence of all research on optimization of apriori algorithms is limiting the frequent itemset 
candidates that are generated by bypassing unwanted transactions so that it does not overtake or repeat 
database scanning excessively; so that it will produce better and faster association rules. 

Apriori algorithm has the disadvantage that it is less efficient on a larger database. Its performance 
will slow down because it has to do a large database scanning with a large number of transactions. 
Iteration is done repeatedly to get the frequent itemset combination in forming the right association 
rules. Modification techniques are needed to optimize the performance of apriori algorithms so as to 
get frequent itemset to form association rules in a short time [22][23][24][25][26][27][28][29]. 
Modifications in this study are obtained through combining combination reduction and iteration 
limitation techniques. 



 A.W.O. Gama and N.M. Widnyani / Knowledge Engineering and Data Science 2020, 3 (2): 89–98 91 

 

II. Method 

A. Association Analysis 

The association method is often used to analyse the contents of a consumer shopping cart in a 
transaction process [30][31]. The association method is also known as the market basket analysis. A 
simple example of an association method application is an analysis of a product purchased at a clothing 
store. The results will be obtained in the analysis, for example, the degree of possibility of consumers 
buying Trousers and clothes together. The application of the association method in the example can 
later help the shop owner to arrange the placement of goods and the inventory, or to make a promotion 
by giving special discounts for the combination of items that are often purchased. 

Association analysis can be explained as a process to explore association rules that meet minimum 
support and minimum confidence requirements, where support and confidence are explained as 
follows: 

1. Analysis of high frequency patterns, at this stage, is looking for item combinations that meet 
the minimum requirements of the support value in the database. The support value of an item 
is obtained by the following formula: 

𝑆𝑢𝑝𝑝𝑜𝑟𝑡 (𝐴) =
𝐿𝑜𝑡𝑠 𝑜𝑓 𝑃𝑢𝑟𝑐ℎ𝑎𝑠𝑒 𝐼𝑡𝑒𝑚 𝐴

𝑇𝑜𝑡𝑎𝑙 𝑃𝑢𝑟𝑐ℎ𝑎𝑠𝑒
 (1) 

The support value of 2 items is explained by the formula below: 

𝑆𝑢𝑝𝑝𝑜𝑟𝑡 𝐴, 𝐵 = 𝑃(𝐴 ∩ 𝐵)  (2) 

𝑆𝑢𝑝𝑝𝑜𝑟𝑡 𝐴, 𝐵 =
∑ 𝑃𝑢𝑟𝑐ℎ𝑎𝑠𝑒 𝐼𝑡𝑒𝑚𝑠 𝐴 𝑎𝑛𝑑 𝐵

∑ 𝑃𝑢𝑟𝑐ℎ𝑎𝑠𝑒
  (3) 

2. Formation of Association rules is sought after all high frequency patterns have been found: 
those which meet the minimum requirements for confidence by calculating the confidence 

value of associative rules A → B.  
The confidence value of A → B rules is obtained from the formula as following: 

𝐶𝑜𝑛𝑓𝑖𝑑𝑒𝑛𝑐𝑒 = 𝑃(𝐵|𝐴) =
∑ 𝑃𝑢𝑟𝑐ℎ𝑎𝑠𝑒 𝐼𝑡𝑒𝑚𝑠 𝐴 𝑎𝑛𝑑 𝐵

∑ 𝑃𝑢𝑟𝑐ℎ𝑎𝑠𝑒 𝐼𝑡𝑒𝑚 𝐴
  (4) 

The following is an example of clothing sales data. Each transaction data written as in the table 1. 
The sales data on Table 1 is translated into tabular forms 1-itemset as in the Table 2. The results of 
the translation will be used to form the next candidates (k + 1) –itemset. A combination of 2-itemsets 
that might be obtained by pairing one product with another product from Table 2, then calculating the 
number of occurrences in each transaction by scanning the database. The result of the combination 
written as the Table 3. 

Table 1. Sales table 

Id Date Clothing Name 

1 2017-08-01 Jacket, T-Shirt 

2 2017-08-01 T-Shirt, Shirt, Trousers 

3 2017-08-01 Shirt, Trousers 

4 2017-08-01 Shirt, Shorts, Trousers 

5 2017-08-01 Shirt, Trousers, Jacket, T-Shirt  
 

 

Table 2. Description of transactions to form 1-itemset 

No Jacket T-Shirt Shirt Trousers Shorts  

1 1 1 0 0 0 

2 0 1 1 1 0 

3 0 0 1 1 0 

4 0 0 1 1 1 

5 1 1 1 1 0 

Total 2 3 4 4 1 
 



92 A.W.O. Gama and N.M. Widnyani / Knowledge Engineering and Data Science 2020, 3 (2): 89–98 

Table 3 shown the prospective of 2-itemsets Candidates. If the threshold value (min_support) = 2 
is obtained for the candidate on Table 3, frequent 2-itemset is as follows 

F2 =  
Jacket, T-Shirt 
T-Shirt, Shirt  
T-Shirt, Trousers 
Shirt, Trousers 

The frequent 3-itemsets candidates are formed in the same way. Similar method is used in pairing 
item one with other items to form a 3-itemset candidate as in the Table 3. 

The threshold value (min_support) has been predetermined = 2. Therefore frequent 3-itemset from 
Table 4 is obtained as follows: 

F3 = T-Shirt, Shirt, Trousers 

If (k + 1) -itemset that can be formed no longer exists, the support and confidence value for each 
frequent itemset combination is calculated. Association rules are formed based on selected frequent 
(k + 1) -itemset. 

The selected association rules are a rule that has a confidence value greater than or equal to the 
min_confidence value. The min_confidence value is 80%. The following Table 7 forms the 
association rules on Table 5 and Table 6. 

The Rules of Final Association shown on Table 7 aims to choose the most suitable rules as a guide 
to improve decision making and marketing strategies. This stage produces output in the form of 
frequent itemset or rule with the highest multiplication of support and confidence value. This stage is 
the final conclusion of the apriori process which later explains that association rules with the strongest 
influence are rules that have the highest multiplication of support and confidence values. 

Table 3. Prospective of 2-itemset candidates 

Combination  Number 

Jacket T-Shirt 2 

Jacket Shirt 1 

Jacket Trousers 1 

Jacket Shorts 0 

T-Shirt Shirt 2 

T-Shirt Trousers 2 

T-Shirt Shorts 0 

Shirt Trousers 4 

Shirt Shorts 1 

Trousers Shorts 1 

 

Table 4. Prospective of 3-itemset candidate 

Combination   Number 

Jacket T-Shirt Shirt 1 

Jacket T-Shirt Trousers 1 

Jacket T-Shirt Shorts 0 

Jacket Shirt Trousers 1 

Jacket Shirt Shorts 0 

Jacket Trousers Shorts 0 

T-Shirt Shirt Trousers 2 

T-Shirt Shirt Shorts 0 

T-Shirt Trousers Shorts 0 

Shirt Trousers Short 1 

 



 A.W.O. Gama and N.M. Widnyani / Knowledge Engineering and Data Science 2020, 3 (2): 89–98 93 

 

Apriori algorithm uses all items in the database transaction every time the process of the scanning 
database generates combinations. It is very timely inefficient, because the items that rarely appear are 
still used in forming combinations. Figure 1 shows the flowchart of apriori algorithm. 

The explanation of the flowchart on Figure 1 can be described as follows: 

1. Determining the minimum support and minimum confidence value using approximate values 
by trial and error. In this research, this has been determined for minimum support = 2 and 
minimum confidence = 80% 

2. Apriori Algorithm using the iterative approach for k-itemset is generated to form the next (k 
+ 1) –itemset. 

3. (k + 1) -itemset candidates which are formed with frequencies that rarely appear in the 
database or below the threshold (min_support) will be eliminated and not used in determining 
association rules. 

4. 1-itemset is formed by scanning a database and then the number of occurrences of each item 
on each transaction is counted. 

5. Furthermore, the itemset is used to form 2-itemsets. Candidates for 2-itemset are formed by 
pairing one item with another item so that it forms a 2-itemset combination. 

6. The value of 2-itemsets that have been formed is then calculated for its appearance on every 
transaction. The threshold (min_support) value is determined to eliminate candidates that are 
not frequent. 

7. The support and confidence values of the 2-itemset that qualify are then calculated. 2-itemset 
whose support and confidence values are above or equal to min_support and min_confidence 
will be used to form association rules. 

8. Then iteration is repeated by using formed 2-itemset to find 3-itemset and so on until there is 
no more frequent (k + 1)-items left. 

9. After all association rules from frequent (k + 1) -itemset are formed, then the values of support 
and confidence are calculated. Multiplication results from the highest support and confidence 
values are the best association rules of all transactions in the database.  

Table 5. Prospective association rules of F2 

IF Antecedent, then Consequent Support Confidence 

If Jacket, then T-Shirt 2 / 5 = 40% 2 / 2 = 100% 

If T-Shirt, then Jacket 2 / 5 = 40% 2 / 3 = 66.7% 

If T-Shirt, then Shirt 2 / 5 = 40% 2 / 3 = 66.7% 

If Shirt, then T-Shirt 2 / 5 = 40% 2 / 4 = 50% 

If T-Shirt, then Trousers 2 / 5 = 40% 2 / 3 = 66.7% 

If Trousers, then T-Shirt 2 / 5 = 40% 2 / 4 = 50% 

If Shirt, then Trousers 4 / 5 = 80% 4 / 4 = 100% 

If Trousers, then Shirt 4 / 5 = 80% 4 / 4 = 100% 

 

Table 6. Candidate rules association of F3 

IF Antecedent, then Consequent Support Confidence 

If T-shirt and shirts, then Trousers 2/5 = 40% 2/2 = 100% 

If T-Shirt and Trousers, then Shirt 2/5 = 40% 2/2 = 100% 

If Shirt and Trousers, then T-Shirt 2/5 = 40% 2/4 = 50% 

 

Table 7. Rules of Final Association 

If Antecedent, then Consequent Support Confidence Support * Confidence 

if buy Jacket, then buy T-Shirt 2 / 5 = 40% 2 / 2 = 100% 0.4 

if buy Shirt, then buy Trousers 4 / 5 = 80% 4 / 4 = 100% 0.8 

if buy Trousers, then buy Shirt 4 / 5 = 80% 4 / 4 = 100% 0.8 

if buy T-Shirt and Shirt, then buy Trousers 2 / 5 = 40% 2 / 2 = 100% 0.4 

if buy T-Shirt and Trousers, then buy Shirt  2 / 5 = 40% 2 / 2 = 100% 0.4 

 



94 A.W.O. Gama and N.M. Widnyani / Knowledge Engineering and Data Science 2020, 3 (2): 89–98 

Apriori algorithm has the disadvantage that it is less efficient on a larger database. The apriori 
algorithm performance will slow down because it has to perform an extensive database scanning with 
a large number of transactions and repeated iterations to get the combination of frequent itemset so 
that it forms the right association rules. These weaknesses can be overcome by applying modification 
techniques on the formation of candidates of the frequent itemset combination. 

B. Combination Reduction 

The modified algorithm in this study employs methods of reduction combination or different 
generated reduction combinations. Combination reduction handles frequent itemset or a combination 
of the results of the previous scanning database to form the next itemset candidate. The generated 
combination then contains frequent itemset from the results of previous scanning database. The 
combination formed by this method is certainly fewer than combinations that are formed by apriori 
method without modification and have more opportunities to become frequent itemset because the 
combination used to form the next itemset is a frequent itemset. Apriori method without modification 
consumes more time because of repeated scanning to generate all combinations without regard to the 
previous frequent itemset. 

Start

Set min_sup
Set min_conf

Scan database
to generate 1-itemset

k = k + 1

Scan database
to generate candidate  of k-itemset

Generated set = 
null ?

Sca n the databa se to calculate the number 
of occurrences (S) of each k-itemset

No

Delete k-itemset with S < min_sup

Stop

Yes

Calculate the support and 
confidence for each k-itemset

Delete k-itemset < min_conf

Output
Association rule

Input Transaction 
Database

 

Fig. 1. Flowchart of Apriori Algorithm [11] 

 



 A.W.O. Gama and N.M. Widnyani / Knowledge Engineering and Data Science 2020, 3 (2): 89–98 95 

 

1) Specifying Items that are used to Generate Combinations (1-Items) 

Finding 1-itemset has to be completed before generating a possible combination that appears.  
The 1-itemsets must qualify the minimum support emergence that will be used to form 
combinations in the search for frequent itemset. 1-itemset is searched by scanning the database and 
accumulating the number of occurrences of each item in all transactions. Items whose occurrence 
values are less than the minimum support are not used in determining the combination of (k + 1)-
itemset while items that are qualified will be used as a combination pair in forming the next 
itemsets. 

2) Generating Itemset Combinations based on Previous Frequent Itemset 

After obtaining frequent itemset from 2-itemset resulting from the initiate database scanning, 
then the combination 3-itemset candidate is generated by simply pairing frequent itemset from 2-
itemset with other items that meet the minimum support. 3-combination itemset candidates that do 
not contain frequent itemset from 2-itemset and items unqualified for the minimum support do not 
need to be generated. This will result in considerable time saving, low computing and avoiding the 
memory allocation to run out. 

For example; shorts Item in the Table 8 above are removed. This is because the occurrence values 
less then minimum support = 2. After going through the process of forming a combination with apriori 
algorithm, frequent 2-itemset is obtained from the scanning database, namely:  

F2 =  
1. Jacket, T-Shirt 
2. T-Shirt, Shirt 
3. T-Shirt, Trousers 
4. Shirt, Trousers 

Results from frequent 2-itemset then is used to make 3-itemset candidate. Iteration is obtained 
through similar previous method by pairing just a combination that includes frequent 2-itemset only 
with one other item that meets the minimum support. The 3-itemset candidates are obtained as in the 
Table 9 which illustrates that combinations are generated only for those that contain frequent itemset 
of 2-itemset paired with other items with minimal support qualification. Consequently, the unqualified 
items have been removed. This combination reduction will reduce computation in forming 
combinations so that it saves time and accelerates the apriori algorithm to find association rules. 

Iteration in apriori algorithms is not limited until all combinations of generated itemset appear in 
transaction data; in which in this case are as many as the number of items contained in the transaction. 
The application of iteration limitation here is to limit the repetition of the scanning database in 
generating a combination of (k + 1) -itemset. It is obtained by using the mode formula to find out how 
many items are often purchased in one transaction that often occurs. 

Table 8. Items that Meet the Minimum Support 

No Jacket T-Shirt Shirt Trousers 

1 1 1 0 0 

2 0 1 1 1 

3 0 0 1 1 

4 0 0 1 1 

5 1 1 1 1 

Number 2 3 4 4 

 

Table 9. Prospective 3-Itemset Candidates 

Combination   Amount 

Jackets T-Shirt Shirts 1 

Jackets T-shirts Trousers 1 

Jackets Shirts Trousers 1 

T-Shirt Shirt Trousers 2 

 



96 A.W.O. Gama and N.M. Widnyani / Knowledge Engineering and Data Science 2020, 3 (2): 89–98 

In the example, 100 transaction samples and 25 items that meet the minimum support are exercised, 
and the number of transactions that often appear are as many as 2 items in the transaction. Most 
consumers buy 2 items in one transaction. This can be used as an iterative delimiter where up to 2 
frequent itemset in accordance with the habits are often done by consumers and this process faster and 
more efficient. 

Based on the transaction data on Table 10, the set size of transactions that often appear is set size 
= 2 and set size = 4, then the value used is the largest value: k = 4 because the possibility of getting 
the best association rules becomes greater. Iteration to search (k + 1) -itemset with apriori algorithm 
will be halted until no more frequent itemset and the maximal limit of iteration is k <= 4. 

III. Results and Discussions 

This study was conducted to determine the results of the comparison of apriori algorithm without 
modification and with a modified apriori algorithm. Modifications of apriori algorithm are expected 
to be faster in generating association rules so that they are more time-efficient. 

Ratio results are measured in terms of required time between apriori algorithms without 
modification with the apriori algorithm that have been modified. Both algorithms are exercised with 
several database samples with the number of transactions that continue to grow and each experiment’s 
required time is calculated until it establishes the association rule. The algorithm’s required time is 
obtained from the algorithm’s expiry time calculation. This aims to obtain the algorithm of time-
reduced association rules which is executed in accordance with the formula as follows: 

$𝑟𝑒𝑞𝑡𝑖𝑚𝑒 = $𝑡_𝑠𝑡𝑎𝑟𝑡𝑡𝑖𝑚𝑒 − $𝑡_𝑒𝑛𝑑𝑡𝑖𝑚𝑒  (5) 

The results of the required time comparison of the algorithm can be recorded in Table 11 which 
shows comparison on data sample 400 and 500 in Apriori without modification is failed because the 
database server is error (time out). The memory bandwidth cannot accommodate the large iteration of 
data. The result graphs of measurement in terms of time and number of transactions which shown in 
Figure 2: 

Graph of research time comparison on Figure 2 shows that the apriori algorithm that has been 
modified is more time efficient in order to obtain association rules. The horizontal data show the 
number of transactions while the vertical data shows the required time to get the association rules. The 
red lines represent the development of the results of apriori with modification, while the blue lines 
represent the development of the results of the apriori without modification. Apriori without 
modification represented by a blue line shows a sharp increase, meaning that the more data increases, 
the higher the computation in the combination formation process and the more time needed to obtain 

Table 11. Comparison of apriori algorithm time with multiple sample transaction 

No. Sample Transaction 
Required time (in microseconds) 

Apriori Apriori Modification 

1 100 6.16 0.81 

2 200 144.90 5.16 

3 300 942.87 18.08 

4 400 Failed 36.71 

5 500 Failed 75.64 

 

Table 10. Transaction Data with Set Size for Iteration Limitation 

TId Transaction Name Item Name Set Size 

1 2013-06-10 Jacket, T-Shirt 2 

2 2013-06-10 T-Shirt, Shirt, Trousers 3 

3 2013-06-10 Shirt, Trousers 2 

4 2013-06-10 Shirt, Shorts, T-Shirt, Trousers 4 

5 2013-06-10 Shirt, Trousers, Jacket, T-Shirt 4 

 



 A.W.O. Gama and N.M. Widnyani / Knowledge Engineering and Data Science 2020, 3 (2): 89–98 97 

 

frequent itemset. The red line shows an increase that is not too sharp and tends to be flat, meaning that 
even though the transaction data continues to increase the required time is proportional to the increase 
in transaction data. 

The results of several trials with several transaction samples show that the quality of association 
rules obtained by apriori modification algorithms is no different from unmodified algorithms. The 
association rules obtained from the apriori algorithm without modification with the modified 
association algorithm are the same in several attempts. The results of the experiment show that there 
is no quality degradation from the established association rules. 

IV. Conclusion 

Apriori algorithm is suitable to be applied to transactions in large database to find frequent itemset. 
Association rules that result from frequent itemset can then be used for improving decisions in 
organizing item displays, arranging inventory or promotion strategies with the example of applying 
discounts for combination items that often appear in transactions according to the established 
association rules. Apriori performance that slows down in larger databases can be optimized by using 
modification method. Apriori algorithm that has been modified with combination reduction and 
iteration limitation techniques has proven to be more efficient in terms of time than the performance 
of unmodified algorithms in generating association rules. The quality of the resulting rules is also 
unchanged, in other words the results obtained are similar between the apriori algorithm without 
modification and the modified apriori algorithm. 

Declarations  

Author contribution  

All authors contributed equally as the main contributor of this paper. All authors read and approved the final paper. 

Funding statement  

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.  

Conflict of interest  

The authors declare no conflict of interest.  

Additional information  

No additional information is available for this paper. 

References 

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[2] P. N. Tan, M. Steinbach, and V. Kumar, Introduction to Data Mining. United States of America: Pearson Addison-
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[3] J. Pamungkas and Y. Handrianto, “Assosiation Rules for Product Sales Data Analysis Using The Apriori Algorithm,” 
Sink. Junal Penelit. Tek. Inform., vol. 5, no. 1, p. 84, 2020. 

 

Fig. 2. Time comparison chart of apriori algorithms 

https://doi.org/10.1609/aimag.v17i3.1230
https://doi.org/10.1609/aimag.v17i3.1230
https://www.pearson.com/us/higher-education/program/Tan-Introduction-to-Data-Mining/PGM93748.html
https://www.pearson.com/us/higher-education/program/Tan-Introduction-to-Data-Mining/PGM93748.html
https://doi.org/10.33395/sinkron.v5i1.10599
https://doi.org/10.33395/sinkron.v5i1.10599


98 A.W.O. Gama and N.M. Widnyani / Knowledge Engineering and Data Science 2020, 3 (2): 89–98 

[4] R. Agrawal, “Mining Association Rules between Sets of Items in Large Databases,” in Proceeding of the 1993 ACM 
SIGMOD Conference Washington DC, USA, 1993, pp. 1–10. 

[5] R. Agrawal and R. Srikant, “Fast Algorithms for Mining Association Rules,” Proceeding 20th VLDB Conf. Santiago, 
Chile., 1994. 

[6] J. Han and M. Kamber, Data Mining: Concepts and Techniques Second Edition. United States of America: Elsevier 
Inc., 2006. 

[7] L. F. Panjaitan, Y. Handrianto, and A. Nurhadi, “Apriori Algorithm On Car Rental Analysis With The Most Popular 
Brands,” Sink. Junal Penelit. Tek. Inform., vol. 4, no. 2, p. 47, 2020. 

[8] E. Irfiani, “Application of Apriori Algorithms to Determine Associations in Outdoor Sports Equipment Stores,” Sink. 
Junal Penelit. Tek. Inform., vol. 3, no. 2, p. 218, 2019. 

[9] G. Danon, M. Schneider, M. Last, M. Litvak, and A. Kandel, “An Apriori-like algorithm for Extracting Fuzzy 
Association Rules between Keyphrases in Text Documents,” Cs.Bgu.Ac.Il, 2006. 

[10] Luthfiah and K. Ditha Tania, “K-Means and apriori algorithm for pharmaceutical care medicine (case study: Eye 
hospital of South Sumatera Province),” in Journal of Physics: Conference Series, 2019, pp. 1–7. 

[11] A. Ezhilvathani and K. Raja, “Implementation of Parallel Apriori Algorithm on Hadoop Cluster,” Int. J. Comput. Sci. 
Mob. Comput., vol. 2, no. 4, pp. 513–516, 2013. 

[12] N. A. Harun, M. Makhtar, A. A. Aziz, Z. A. Zakaria, F. S. Abdullah, and J. A. Jusoh, “The application of Apriori 
algorithm in predicting flood areas,” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 7, no. 3, pp. 763–769, 2017. 

[13] N. Badal and S. Tripathi, “Frequent Data Itemset Mining Using VS _ Apriori Algorithms,” Int. J. Comput. Sci. Eng., 
vol. 2, no. 4, pp. 1111–1118, 2010. 

[14] J. Suresh and T. Ramanjaneyulu, “Mining Frequent Itemsets Using Apriori Algorithm,” Int. J. Comput. Trends Technol., 
vol. 4, no. 4, pp. 760–764, 2013. 

[15] S. A. Abaya, “Association Rule Mining based on Apriori Algorithm in Minimizing Candidate Generation,” Int. J. Sci. 
Eng. Res., vol. 3, no. 7, pp. 1–4, 2012. 

[16] J. Yabing, “Research of an Improved Apriori Algorithm in Data Mining Association Rules,” Int. J. Comput. Commun. 
Eng., vol. 2, no. 1, pp. 25–27, 2013. 

[17] J. Singh, H. Ram, and J. S. Sodhi, “Improving Efficiency of Apriori Algorithm Using Transaction Reduction,” Int. J. 
Sci. Res. Publ., vol. 3, no. 1, pp. 1–4, 2013. 

[18] J. Silva, N. Varela, L. A. B. López, and R. H. R. Millán, “Association rules extraction for customer segmentation in the 
SMES sector using the apriori algorithm,” in Procedia Computer Science, 2019, pp. 1207–1212. 

[19] A. W. O. Gama, I. K. G. D. Putra, and I. P. A. Bayupati, “Implementasi Algoritma Apriori untuk Menemukan Frequent 
Itemset dalam Keranjang Belanja,” Tekonologi Elektro, vol. 15, no. 2, pp. 27–32, 2016. 

[20] K. K. Widiartha, D. Putu, and D. Kumala, “Shopping Cart Analysis System in Product Layout Management with Apriori 
Algorithm,” Int. J. Appl. Comput. Sci. Inform. Eng., vol. 1, no. 2, pp. 53–64, 2019. 

[21] K. S. Raju, A. D. Devi, and D. D. D. Suribabu, “Mining Frequent Item Sets Using Apriori Algorithm on Shopping 
Dataset,” Mukth Shabd J., vol. 9, no. 5, pp. 6309–6320, 2020. 

[22] B. Patel, V. K. Chaudhari, R. K. Karan, and Y. . Rana, “Optimization of Association Rule Mining Apriori Algorithm 
Using ACO,” Int. J. Soft Comput. Eng., vol. 1, no. 1, pp. 24–26, 2011. 

[23] M. F. Akas, A. G. M. Zaman, and A. Khan, “Combined item sets generation using modified apriori algorithm,” in ACM 
International Conference Proceeding Series, 2020, pp. 4–6. 

[24] H. Yu, J. Wen, H. Wang, and J. Li, “An improved Apriori algorithm based on the Boolean matrix and Hadoop,” 
Procedia Eng., vol. 15, pp. 1827–1831, 2011. 

[25] Z. Jie and W. Gang, “Intelligence Data Mining Based on Improved Apriori Algorithm,” J. Comput., vol. 14, no. 1, pp. 
52–62, 2019. 

[26] X. Liu, Y. Zhao, and M. Sun, “An Improved Apriori Algorithm Based on an Evolution-Communication Tissue-Like P 
System with Promoters and Inhibitors,” Discret. Dyn. Nat. Soc., vol. 2017, 2017. 

[27] R. Sun and Y. Li, “Applying Prefixed-Itemset and Compression Matrix to Optimize the MapReduce-based Apriori 
Algorithm on Hadoop,” in ACM International Conference Proceeding Series, 2020, pp. 89–93. 

[28] X. Yuan, “An improved Apriori algorithm for mining association rules,” in AIP Conference Proceedings, 2017, pp. 1–
6. 

[29] D. T. Larose, An introduction to data mining, vol. 134. Canada: John Wiley & Sons, Inc, 2005. 

[30] Y. Kurnia, Y. Isharianto, Y. C. Giap, A. Hermawan, and Riki, “Study of application of data mining market basket 
analysis for knowing sales pattern (association of items) at the O! Fish restaurant using apriori algorithm,” in Journal 
of Physics: Conference Series, 2019, pp. 1–6. 

[31] J. R. Delos Arcos and A. A. Hernandez, “Analyzing online transaction data using association rule mining: Misumi 
philippines market basket analysis,” in ACM International Conference Proceeding Series, 2019, pp. 45–49. 

 

https://doi.org/10.1145/170035.170072
https://doi.org/10.1145/170035.170072
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https://www.elsevier.com/books/data-mining-southeast-asia-edition/han/978-0-12-373584-3
https://doi.org/10.33395/sinkron.v4i2.10506
https://doi.org/10.33395/sinkron.v4i2.10506
https://doi.org/10.33395/sinkron.v3i2.10089
https://doi.org/10.33395/sinkron.v3i2.10089
http://www.math.s.chiba-u.ac.jp/~yasuda/open2all/Paris06/IPMU2006/HTML/FINALPAPERS/P617.PDF
http://www.math.s.chiba-u.ac.jp/~yasuda/open2all/Paris06/IPMU2006/HTML/FINALPAPERS/P617.PDF
https://doi.org/10.1088/1742-6596/1196/1/012051
https://doi.org/10.1088/1742-6596/1196/1/012051
https://www.ijcsmc.com/docs/papers/April2013/V2I4201396.pdf
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http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1463
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http://www.enggjournals.com/ijcse/doc/IJCSE10-02-04-73.pdf
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http://ijcttjournal.org/archives/ijctt-v4i4p164
https://www.ijser.org/onlineResearchPaperViewer.aspx?Association-Rule-Mining-based-on-Apriori-Algorithm-in-Minimizing-Candidate-Generation.pdf
https://www.ijser.org/onlineResearchPaperViewer.aspx?Association-Rule-Mining-based-on-Apriori-Algorithm-in-Minimizing-Candidate-Generation.pdf
http://dx.doi.org/10.7763/IJCCE.2013.V2.128
http://dx.doi.org/10.7763/IJCCE.2013.V2.128
http://www.ijsrp.org/research-paper-1301/ijsrp-p1397.pdf
http://www.ijsrp.org/research-paper-1301/ijsrp-p1397.pdf
https://doi.org/10.1016/j.procs.2019.04.173
https://doi.org/10.1016/j.procs.2019.04.173
https://ojs.unud.ac.id/index.php/JTE/article/view/ID19726
https://ojs.unud.ac.id/index.php/JTE/article/view/ID19726
https://doi.org/10.33173/acsie.55
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https://doi.org/10.1145/3377049.3377125
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https://doi.org/10.1016/j.proeng.2011.08.340
https://doi.org/10.1016/j.proeng.2011.08.340
https://doi.org/10.17706/jcp.14.1.52-62
https://doi.org/10.17706/jcp.14.1.52-62
https://doi.org/10.1155/2017/6978146
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https://doi.org/10.1145/3384544.3384610
https://doi.org/10.1145/3384544.3384610
https://doi.org/10.1063/1.4977361
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http://dx.doi.org/10.1002/0471687545
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https://doi.org/10.1145/3377170.3377226
https://doi.org/10.1145/3377170.3377226

	I. Introduction
	II. Method
	A. Association Analysis
	B. Combination Reduction
	1) Specifying Items that are used to Generate Combinations (1-Items)
	2) Generating Itemset Combinations based on Previous Frequent Itemset


	III. Results and Discussions
	IV. Conclusion
	Declarations
	Author contribution
	Funding statement
	Conflict of interest
	Additional information

	References
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