Securing association rule mining with FP growth algorithm in horizontally partitioned database

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.

Association rule mining is one of the fundamental research topics in data mining and knowledge discovery that identifies interesting relationships between itemsets in datasets and predicts the associative and correlative behaviors for new data. Rooted in market basket analysis, there are a great number of techniques developed for association rule mining. They include frequent pattern discovery, interestingness, complex associations, and multiple data source mining. This paper introduces the up‐to‐date prevailing association rule mining methods and advocates the mining of complete association rules, including both positive and negative association rules. © 2011 John Wiley & Sons, Inc.WIREs Data Mining Knowl Discov2011 1 97‐116 DOI: 10.1002/widm.10This article is categorized under:Algorithmic Development > Association Rules

We explored the potential association rules within acupoints in treating diabetic gastroparesis (DGP) using Apriori algorithm complemented with another partition-based algorithm, a frequent pattern growth algorithm. Apriori algorithm is a data mining-based analysis that is widely applied in various fields, such as business and medicine, to mine frequent patterns in datasets. To search for effective acupoint combinations in the treatment of DGP, we implemented Apriori algorithm to investigate the association rules of acupoints among 17 randomized controlled trials (RCTs). The acupoints were extracted from the 17 included RCTs. In total, 29 distinct acupoints were observed in the RCTs. The top 10 frequently selected acupoints were CV12, ST36, PC6, ST25, BL21, BL20, BL23, SP6, BL18, and ST21. The frequency pattern of acupoints achieved by using a frequent pattern growth algorithm also confirms the result. The results showed that the most associated rules were {BL23, BL18} ≥ {SP6}, {BL20, BL18} ≥ {PC6}, {PC6, BL18} ≥ {BL20}, and {SP6, BL18} ≥ {BL23} in the database. Acupoints, including BL23, BL18, SP6, BL20, and PC6, can be deemed as core elements of acupoint combinations for treating DGP.

In a business, it takes effort to maximize profits. Among them by doing promotions. Minimarket Ulfamart has not done innovation in the form of promotion of frugal packages to customers. The precision of promotion can be learned from the database of a retail company primarily shopping patterns on products commonly purchased simultaneously. Information about inaccurate customer spending patterns results in inaccurate and efficient promotional policies. One of the most common attempts to acquire and explore customer spending patterns is to use data mining known as Knowledge Discovery in Database (KDD). One of the data mining techniques is the Association Rule which is a procedure in Market Basket Analysis. Market basketball is defined as an itemset purchased simultaneously by the customer in a transaction. Market basket analysis is a powerful tool for cross-selling strategies. A pattern is determined by two parameters, namely support (value of support) and confidence (value of certainty). The Frequent Pattern Growth (FP-Growth) algorithm is used to help find some association rules from the database by applying a Tree Tree structure or called FP-Tree. Implementation using RapidMiner to help find accurate patterns to get a combination of items that can be used as a frugal package.

This research aims to explore tourist behaviour patterns and develop data-driven tourism route recommendations based on Association Rule Mining (ARM), particularly using the Frequent Pattern Growth (FP-Growth) algorithm. The study focuses on identifying co-occurrence patterns among tourist attractions based on real behavioral data, with the goal of supporting tourism route design and enhancing recommendations for self-guided tourists. The study employed a structured data mining methodology. Raw data was collected from real-world tourist check-ins and GPS traces in Songkhla City, Thailand; encompassing 45 attraction points. The study used the Cross-Industry Standard Process for Data Mining (CRISP-DM) for research methodology. The FP-Growth algorithm was used to discover association rules, using parameters set at support > 0.10, confidence > 0.5, lift > 1.0, and conviction > 1.0 to ensure the relevance and reliability of the extracted rules. The evaluation phase included interpretation of rule quality metrics such as support, confidence, lift, and conviction indicators. Visualizations presented the route recommendations on geographic maps. The results revealed 13 valid association rules involving only 5 out of the 45 locations, suggesting strong behavioral patterns and repeat co-visitation. Interestingly, some of the top-visited locations based on frequency alone did not appear in any of the 13 rules, indicating that frequency-based route planning may not reflect actual tourist behavior. Further analysis showed that all route combinations extracted from the rules had a total travel distance of under 88 kilometers. With Google Map data, this study demonstrates that ARM with FP-Growth can provide insightful patterns for route planning and destination clustering. The proposed method supports smart tourism development by bridging behavioral analytics and operational tourism planning. Stakeholders can apply this approach to periodically update route recommendations to ensure alignment with current tourist behaviors and preferences.

This article presents a market basket analysis of a health food store in Thailand. The analysis identifies data attributes that frequently occur together in the dataset. Frequent occurrences of data attributes representing customer purchasing behaviors are extracted as association rules using the frequent pattern growth algorithm. The generated associations are evaluated using standard measures based on occurrence counts and an additional financial measure. Marketing strategies in the form of cross-selling pairs of specific products are then designed based on the data attributes appearing in the significant associations. The cross-selling products are offered at discounted prices and promoted in marketing campaigns. A break-even analysis is performed to estimate the required number of additional sales volumes from each marketing campaign to compensate for the discounted prices. The presented use case demonstrates the effectiveness of extending the market basket analysis to include a financial measure that can lead to practical marketing campaigns.

The aim of this research paper is to construct a set of guidelines that can improve the quality and efficiency of knowledge data discovery process by utilizing various types of knowledge domains. In addition, this paper offered the way of how the knowledge domain could be adopted for helping the system developer. The methodologies contain various scenarios of data exploring and the authors used data mining approach. The paper shows evidence of important to adopt data mining methods in the industry sector as well as the advantages and disadvantages. Evergreen human machine interface (HMI) at PT. Chevron Pacific Indonesia (CPI) is kind of activities to maintenance computer equipment. Nowadays, the errors were frequently happened in the accuracy of computer maintenance which has a profound effect on production results. Therefore, this study focuses on the rules of association using the frequent pattern growth algorithm (FP-growth) which is producing knowledge with trust value of 100% and a support value is 95%. The value results of support and confidence are the new approach and knowledge for the management level to decide decisions in the evergreen activities process.

Frequent itemset mining and association rule mining are fundamental problems in data mining. Despite of the intensive and continuous researches on frequent itemset mining, one essential and not completely solved drawback still remains. The drawback is pattern explosion: a huge number of frequent itemsets, or patterns, will be often derived. The association rule mining takes over the same drawback since each association rule consists of two frequent itemsets. One promising solution for alleviating this essential drawback practically as a post-processing is to rank the patterns and rules from various aspects and to identify which one to be examined first. In this paper, we propose to utilize the technology of representation learning to rank frequent itemsets and association rules. More specifically, we develop several evaluation measures for frequent itemsets and association rules by using the vector representations of items, transactions and frequent patterns which are obtained by existing representation learning techniques. By employing vector representations of three kinds of objects, various rankings reflecting different aspects can expect to be obtained.

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.

Parallel extraction of association rules from genomics data

Supermarket analysis examines purchasing patterns to identify relationships among the various goods in a customer's shopping cart. The results of these correlations have assisted businesses in developing a successful sales strategy by grouping goods that customers commonly buy. Due to the expanding volume of data and its widespread utilization in the retail sector to enhance marketing strategies, Data Mining (DM) has become increasingly important in recent years. Transaction data analysis from the past yields a wealth of knowledge about consumer behaviour and commercial choices. The rate at which data is saved doubles every second that the fastest processor is available for its analysis. In a huge dataset or database, the Market Basket Analysis (MBA) approach of DM seeks out a group of items that commonly appear together. This technology is employed in a variety of sectors, including retail, to encourage cross-selling, assist with fraud detection, product replacement, and certain purposes. Based on this technology, it is simple to understand the buying trends of consumers and their preferences. Technology has advanced, and current business practices have significantly changed as a result. By figuring out the connections among the various things in the consumer's buying baskets, this approach examines their purchasing behaviours. Businesses must increase the accuracy of their operations as a result of changes in customer demands. This research focuses on FP growth, which performs better in mining frequent itemsets than apriori. Hence, this paper focuses on analyzing frequent patterns using conditional FP-Tree in FP growth and compares it with improved and traditional apriori with minimum support as the threshold for identifying the frequently occurring item sets. Moreover, the time consumption of the Associative Rule Mining (ARM) model has been compared with the FP Growth algorithm for identifying the short-time comparison model.

Sales data stored in enterprise databases are usually stored as archives or documentation. In the case of retail companies, data mining science can be used to extract new information from sales database, ie consumer purchase pattern analysis. The algorithm that can be used to analyze consumer purchase pattern is FEM algorithm using combination of Frequent Pattern Growth (FP-Growth) and Eclat algorithm. The construction of FP-Tree tree structure is done by using FP-Growth algorithm, while the process of extraction of items purchased (frequent itemset) is done by using Eclat algorithm. The application designed can be used to analyze consumer purchase pattern by generating associative rules using FEM algorithm through the Analysis form and printing the consumer purchase pattern through the purchase pattern report.

Association rule mining is one of the most popular pattern discovery methods used in data mining. Frequent pattern extraction is an essential step in association rule mining. Most of the proposed algorithms for extracting frequent patterns are based on the downward closure lemma concept utilizing the support and confidence framework. In this chapter we investigate an alternative method for mining frequent patterns in a transactional database. Self-Organizing Map (SOM) is an unsupervised neural network that effectively creates spatially organized internal representations of the features and abstractions detected in the input space. It is one of the most popular clustering techniques, and it reveals existing similarities in the input space by performing a topology-preserving mapping. These promising properties indicate that such a clustering technique can be used to detect frequent patterns in a top-down manner as opposed to the traditional approach that employs a bottom-up lattice search. Issues that are frequently raised when using clustering technique for the purpose of finding association rules are: (i) the completeness of association rule set, (ii) the support level for the rules generated, and (iii) the confidence level for the rules generated. We present some case studies analyzing the relationships between the SOM approach and the traditional association rule framework, and propose a way to constrain the clustering technique so that the traditional support constraint can be approximated. Throughout our experiments, we have demonstrated how a clustering approach can be used for discovering frequent patterns.

The discovery of association rules in data mining is an important issue, the core of which is the frequent pattern mining, Apriori algorithm is classical for the association rule mining, but it should repeatedly scan the database and can produce plenty of candidates. By examples, it is proved that Boolean matrix association rules algorithm can improve the algorithmic efficiency by reducing the times of accessing database and without producing candidates.

In this chapter, we present granule based inter association mining to reduce the complexity of intertransaction association rule mining. To compare with other methods, our method can reduce the width of sliding windows. It uses granules to replace extended item sets. Thus, we do not need to consider too many combinations of extended items. We also propose the concept of precision in order to evaluate the effectiveness of intertransaction association rule mining. The experiments show that the proposed method is promising.