Optimizing Customer Purchase Insights: Apriori Algorithm for Effective Product Bundle Recommendations

A supermarket must have good business plan in order to meet customer desires. One way that can be done to meet customer desires is to find out the pattern of shopping purchases resulting from processing sales transaction data. Data processing produces information related to the function of the association between items of goods temporarily. Association rules functions in data mining.Association rule is one of the data mining techniques used to find patterns in combination of transaction data. Apriori algorithm can be used to find association rules. Apriori algorithm is used to find frequent itemset candidates who meet the support count. Frequent itemset that meets the support count is then processed using the temporal association rules method. The function of temporal association rules is as a time limitation in displaying the results of frequent itemsets and association rules. This study aims to produce rules from transaction data, apriori algorithm is used to form temporal association rules. The final results of this research are strong rules, they are rules that always appear in 3 years at certain time intervals with limitation on support and confidence, so that the rules can be used for business plan layout recommendations in Maharani Supermarket Demak.

Culture Coffee MSME is one of the MSMEs engaged in the culinary field and is experiencing business competition. A marketing strategy is needed with the right decision-making process so that the business can survive and excel. UMKM Culture Coffee uses a point of sales application to accommodate the transaction process and record transactions. Historical customer data can be processed into a basis for decision making for marketing strategies that effectively increase sales. However, the transaction data has not been used optimally. There is a need to analyze historical customer data that can generate information to form marketing strategies. Market Basket Analysis (MBA) is one of the methods in data mining used in knowing products that tend to be purchased together by customers known as Association Rule. Association rules produce products in the form of packages or bundling which are used as marketing strategies. The marketing strategy obtained is supported by data visualization which contains information from the data. Apriori algorithm is used to generate association rules. The result of this research is an association rule on the historical data of MSME Culture Coffee customer purchases. Based on these rules, recommendations for selling menu packages to customers can be given. The purpose of this research is to find customer purchasing patterns which are used as the basis for decision making in determining menu sales. The results showed 2 product packages, namely, nuggets and french fries with sausages and french fries with a support and confidence value of 12.5% and 37.6% with 10.8% and 29% respectively. The results of this study can be used as a basis for the sales and marketing strategy of Culture Coffee MSMEs to increase business revenue.

This research project focuses on the design and development of a web-based Point of Sale (POS) application that incorporates advanced analytical techniques, specifically Market Basket Analysis with the Apriori algorithm and the Association Rule method. The primary objective of this web-based POS system is to empower retailers in managing their sales transactions efficiently and gaining valuable insights into customer purchasing behavior. The web-based POS application is multifaceted, offering features such as sales transaction recording, product inventory management, and customer data tracking. What sets it apart is the integration of the Apriori algorithm and Association Rule method, which enable the system to analyze and understand customer purchasing patterns. It identifies strong product associations and establishes rules that support intelligent decision-making for businesses. The advantages of Market Basket Analysis are substantial. Retailers can identify relevant purchase patterns, such as frequently co-purchased products or cross-selling opportunities. This information can be used to enhance marketing strategies, optimize product placement in stores, and create bundled product offerings, ultimately boosting sales and revenue. By analyzing transaction data and recognizing patterns, retailers can streamline their operations, minimize wastage, and allocate resources more effectively. In summary, this research project showcases the transformative potential of integrating Market Basket Analysis, the Apriori algorithm, and the Association Rule method into web-based POS systems. By doing so, retailers can enhance operational efficiency, boost sales, and improve customer satisfaction, ultimately leading to more successful and competitive businesses in the retail sector.

Indonesia is an equatorial country that has abundant natural wealth from the seabed to the top of the mountains, the beauty of the country of Indonesia also lies in the mountains that it has in various provinces, for example in the province of West Nusa Tenggara known for its beautiful mountain, namely Rinjani. The increase in outdoor activities has attracted many people to open outdoor shops in the West Nusa Tenggara region. Sales transaction data in outdoor stores can be processed into information that can be profitable for the store itself. Using a market basket analysis method to see the association (rules) between a number of sales attributes. The purpose of this study is to determine the pattern of relationships in the transactions that occur. The data used is the transaction data of outdoor goods. The analysis used is the Association Rules with the Apriori algorithm and the frequent pattern growth (FP-growth) algorithm. The results of this study are formed 10 rules in the Apriori algorithm and 4 rules in the FP-Growth algorithm. The relationship pattern or association rule that is formed is in the item "if a consumer buys a portable stove, it is possible that portable gas will also be purchased" at the strength level of the rules with a minimum support of 0.296 and confidence 0.774 at Apriori and 0.296 and 0.750 at FP-Growth.

Pharmacy sales transaction data contain valuable information on customer purchasing patterns; however, in practice, such data are often used merely as operational records, making relationships between purchased drugs difficult to identify. This study analyzes drug purchasing patterns using the Apriori and FP-Growth algorithms based on sales transaction data from Apotek Gadi Lamba Condet for the period January to June 2025. The transaction data were processed through data cleaning, drug name standardization, and transformation into transaction format, resulting in 7,038 transactions with 1,495 drug items. Association rule mining was performed using a minimum support of 0.01 and a minimum confidence of 0.17. The results show that the Apriori and FP-Growth algorithms generate ten identical association rules with the same support, confidence, and lift values, and all rules have lift values greater than one. Paracetamol 500 MG emerges as the most frequently involved drug in the association rules. These findings demonstrate that, for medium-scale pharmacy transaction datasets, Apriori and FP-Growth have equivalent capability in identifying drug purchasing patterns, with the primary difference lying in computational efficiency rather than the quality of the generated patterns.

Due to the increase of retail sales in Egypt and all over the world, came the importance for the managers of supermarkets to develop marketing strategy to maximize their profits, by getting rid of inactive products. Product bundling is one of the most important marketing strategies used to get rid of stock by making integrated bundles of inactive products and demanded products with discount prices. We can do that through our recommendation system and also increase customers' faith by keeping up with their purchase habits changes in low prices. In this study, first association rules are applied to find the best-integrated bundles with optimal suggested bundle size according to customer habits. Second testing these resulting product bundles to eliminate bundles didn't contain Products aim to get rid of them. Finally the given suggested bundles’ elements replaced with stagnant products that are the same kind of product in the bundle but with another trade. During that study algorithms (Apriori and FP-growth) were studied. Although the two algorithms give strong association rules, and the results were so near. But FP-Growth algorithm was more efficiency as Apriori algorithm caused problems with minimum support parameter as, in a small system transaction, big minimum support did not work using it. Also, it makes the memory PC faces memory hangs when minimum support was very low. On the other hand, all of this didn't appear with FP-Growth algorithm, and it was faster in dealing data.

MOY Frozen Food is a retail business located in Kediri Regency, specializing in the sale of frozen food, beverages, and basic necessities. In recent years, the retail industry has faced numerous challenges, including shifts in consumer behavior, technological advancements, and increasing competition. This study addresses the issue of identifying which products are frequently purchased together and determining appropriate recommendations for consumers. To achieve this goal, association rules are employed to discover correlations and co-occurrences among data sets, which facilitate the identification of product relationships within a single transaction. Using the Apriori algorithm with a minimum support threshold of 0.01 and a confidence level of 0.5, implemented in Python, this research successfully generates association rules. The insights derived from these association rules can be leveraged to develop various sales strategies, ultimately enhancing product sales at MOY Frozen Food.

A provided data from a transactional database, has supported technical development which can automatically find product association or items saved in the database. This finding association rules between saved product in database known as mining association rules. There are so many theories and algorithm developed for conducting mining association rules. One of algorithm developed is Apriori algorithm. This method, has a main goal to find the maximum frequent itemset. Next, this frequent itemset will be generated into associative rules which are not shown before in the database, become valuable information for considering materials in the decision process. Apriori algorithm is a interpretation technique of mining association rules, will be implemented into a web based software. On the software test which use in some different data, it’s concluded that time for mining association rules depends on the presence of every item in every transaction, total of transaction, minimum support and minimum confidence. For smaller value of minimum support and minimum confidence that entered, program will generate more association rules, vice versa.

The current government policy is directed towards strengthening the MSME sector to achieve sustainable economic growth. The Ecoprint MSME in Lampung faces several challenges in maintaining the continuity of its business, including a decline in sales, limited capital and raw materials, as well as the unstable demand for ecoprint products, especially those that are still relatively new. This instability makes it difficult for MSMEs to manage inventory, which hinders the production process. The research method to address the issue of applying Business Intelligence in the strategy for developing superior products in ecoprint MSMEs using the Apriori Algorithm can be approached through Business Intelligence methods. The data used in this study is sales transaction data from June to August 2024. The stages of applying Business Intelligence include Justification, Planning, Business Analysis, Design, and Construction. By applying Business Intelligence and the Apriori Algorithm, MSMEs can compete in the digital era and achieve sustainable success. Based on testing results using Rapidminer, three association rules were obtained: Dress, Hijab with a support of 57% and confidence of 100%; Hijab, Dress with a support of 57% and confidence of 80%; and Hijab, Accessories with a support of 57% and confidence of 80%. The Apriori algorithm is capable of identifying consumer purchase patterns, allowing MSMEs to determine the most in-demand products and adjust their production strategy accordingly. The application of the Apriori algorithm on transaction data has resulted in various unique association rules. The key difference between these rules lies in the combinations of related products. Further analysis shows that the performance of the Apriori algorithm is optimal when using a minimum support value of 50% and a minimum confidence of 80%.

Sales of sata bottles are growing and increasing, However, the results of these sales transactions have not been maximally utilized by shop owners. In fact, by using data mining techniques, the collection of data can generate new information. Association rule mining can find interaction patterns between one or more items in a very large data set. This algorithm is widely used in transaction data for purchasing product items at the same time by customers. research objectives to improve sales strategy, by collecting sales patterns that help related parties make sales strategy decisions, recommend products to customers, and maintain product availability. The research method using apriori algorithm data mining system that aims to determine consumer purchasing patterns. The association rule obtained results in 1 product that is often purchased simultaneously, namely Buy Rabbit Bottle, 420ml Clear Bottle, Buy Rabbit Bottle, Glass Straw, and Buy Rabbit Bottle, Nice Glass with a support value of 10% and a confidence of 80% in three frequent itemset and Rabbit Bottle, 420ml Clear Bottle, Rabbit Bottle, Glass Straw, and Nice Glass, 420ml Clear Bottle with a support value of 15% and a confidence of 83% in two frequent itemset.

Tourist attractions are places of interest and visited by tourists because they have certain appeals. These attractions can include natural tourism, cultural tourism, artificial tourism, or agro-tourism. Simalungun Regency offers various interesting tourist spots, ranging from natural beauty such as waterfalls and lakes to rich traditional cultures that reflect local history and customs. However, information about tourist visit patterns remains limited. A deeper understanding of these patterns is crucial, especially for tourism managers in designing effective and sustainable development strategies. Therefore, this study aims to identify associations or relationships between tourist attractions based on tourist visit data. The Apriori algorithm was chosen for its ability to discover high-frequency patterns in large datasets. These patterns are expected to provide valuable insights for tourism managers in designing effective promotional strategies. Apriori is a well-known algorithm in data mining, particularly for finding frequent itemsets using association rule techniques. The algorithm is carried out by determining frequent itemsets, from 1-itemset to multi-itemsets, until the association rules are derived from the pre-selected data. To obtain these frequent itemsets, each selected data must meet the minimum support and minimum confidence requirements so that the resulting association rules are relevant and practically applicable.

Among the many mining algorithms of association rules, Apriori Algorithm is a classical algorithm that has caused the most discussion; it can effectively carry out the mining association rules. However, based on Apriori Algorithm, most of the traditional algorithms existed "item sets generation bottleneck" problem, and are very time-consuming. An enhance algorithm associating which is based on the user interest and the importance of itemsets is put forward by the paper, incorporate item that user is interested in into the itemsets as a seed item, then scan the database, incorporate all other items which are in the same transaction into itemsets, Construct user interest itemsets, reduce unnecessary itemsets; through the design of the support functions algorithm not only considered the frequency of itemsets, but also consider different importance between different itemsets. The new algorithm reduces the storage space, improves the efficiency and accuracy of the algorithm.


 
 
 
 Apriori algorithm is a classical algorithm of association rule mining and widely used for generating frequent item sets. However, the original Apriori algorithm has some limitation such as it needs to scan the dataset many times to discover all frequent itemset and generate huge number of candidate itemset. To overcome these limitations, researchers have made a lot of improvements to the Apriori such as candidate generation, without candidate generation, transaction reduction, partitioning, and sampling. When it comes to mine massive data, these algorithms failed to prove efficiency because limitation of the processing capacity, storage capacity, and main memory constraints. Therefore, parallel and distributed algorithms are developed to perform large-scale computing in ARM on multiple processors. However, the problems with most of the parallel and distributed framework are overheads of managing distributed system, lack of high level parallel programming language, and node failures. Hadoop-MapReduce is an efficient, scalable, and simplified programming model for massive data processing and it also available on cloud environment. Cloud computing offers huge computing resources, and capacities to solve big data challenges. Recently many parallel algorithms have been proposed on Hadoop-MapReduce to enhance the performance of Apriori algorithm but there are some drawbacks: since multiple scan over the dataset is needed to generate candidate itemset, it consume more execution time. The aim of this study is to propose a parallel Transaction Reduction MapReduce Apriori algorithm (TRMR-Apriori) which is reduce unnecessary transaction values and transactions from the dataset in parallel manner to overcome above problems. The experiments show that TRMR-Apriori is able to achieve better execution time to discover frequent itemset those of previous sequential ARM algorithms such as Apriori, AprioriTid, Eclat, and FP-Growth and the previous parallel algorithms such as PApriori, MRApriori, and Modified Apriori with different condition on homogeneous computing environment using Hadoop-MapReduce platform in cloud. Overall, the TRMR-Apriori shows the strength to extract the frequent itemset from massive dataset in cloud.
 
 
 
 

When it comes to association rule mining, all frequent itemsets are first found, and then the confidence level of association rules is calculated through the support degree of frequent itemsets. As all non-empty subsets in frequent itemsets are still frequent itemsets, all frequent itemsets can be acquired only by finding all maximal frequent itemsets (MFIs), whose supersets are not frequent itemsets. In this study, an algorithm, named right-hand side expanding (RHSE), which can accurately find all MFIs, was proposed. First, an Expanding Operation was designed, which, starting from any given frequent itemset, could add items using certain rules and form some supersets of given frequent itemsets. In addition, these supersets were all MFIs. Next, this operator was used to add items by taking all frequent 1-itemsets as the starting point alternately, and all MFIs were found in the end. Due to the special design of the Expanding Operation, each MFI could be found. Moreover, the path found was unique, which avoided the algorithm redundancy in temporal and spatial complexity. This algorithm, which has a high operating rate, is applicable to the big data of high-dimensional mass transactions as it is capable of avoiding the computing redundancy and finding all MFIs. In the end, a detailed experimental report on 10 open standard transaction sets was given in this study, including the big data calculation results of million-class transactions.

Analysis and research on library user behavior based on apriori algorithm