Multi-Objective Optimization for High-Dimensional Maximal Frequent Itemset Mining

Frequent itemsets mining is one of the interesting applications of data mining. Recently data mining has got a great deal of attention due to the explosive growth in data and the economic and scientific need for turning such data into useful information. However, the traditional frequent itemsets mining algorithms have become inefficient to work with large datasets effectively on a single machine due to computational power and memory limits. Current methods prefer to control the execution time and output by using higher minimum support thresholds, which lead to less candidates and frequent itemsets. In this paper, an improved-version of Apriori like HFDM-EB algorithm that can deal with lower minimum support thresholds is proposed for mining frequent itemsets over big transactional data on Hadoop framework and by utilizing compressed bitmaps. The experimental results show that the improved algorithm is efficient and scalable for mining frequent itemsets in big data.

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.


 
 
 
 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.
 
 
 
 

This study is motivated by the problem faced by Toko Polirindo, where sales transaction data are stored only as archives and have not been utilized for analytical purposes, resulting in unstable product availability, recurring stock shortages, and difficulties in predicting customer purchasing behavior; therefore, this research aims to identify patterns of item associations that frequently occur together by applying the Association Rule Mining method using the FP-Growth algorithm, which is recognized for its ability to extract frequent itemsets efficiently without the need to generate candidate combinations as in the Apriori algorithm. The dataset consists of sales transactions recorded from January to September 2025. It undergoes several stages, including preprocessing, binary transformation, and analysis using RapidMiner to generate frequent itemsets and association rules, evaluated using support, confidence, and lift metrics. The results reveal that item 3 consistently appears as the most dominant consequent across almost all generated rules, with confidence values ranging from 0.322 to 0.347, indicating that this item is most strongly associated with other items and frequently appears as a complementary product in customer transactions. These findings provide practical contributions by offering insights to optimize stock management, improve product placement, and develop promotional strategies based on actual purchasing patterns, while also demonstrating that the FP-Growth algorithm is an effective analytical tool to support data-driven decision-making aimed at enhancing operational efficiency and customer satisfaction in retail environments.

With the advent of the era of big data, intelligent manufacturing is becoming the developing direction of textile and clothing industry. Many textile enterprises are exploring the intelligent techniques to improve production efficiency and product quality. This paper focuses on yarn quality prediction based on the big data of spinning production. First, the association rules algorithms, Aproiori and I_Apriori algorithms, which are commonly used for intelligent prediction in spinning production, are analyzed. Then, aiming to overcome their disadvantages such as low efficiency, time consuming, big error of prediction results under big data, a global optimization strategy based on genetic algorithm is proposed. This strategy optimizes the global search process of Aprioir algorithm for pruning the frequent itemsets by introducing the genetic algorithm, which can avoid the local optimal solution in the search process. Finally, based on the big real production data collected from the spinning factory, the effectiveness of the proposed Apriori algorithm are investigated and compared with the normal Apriori algorithm. The result indicates that the improved algorithm has better efficiency and more accurate prediction results and is good at dealing with the big data environment.

Most retail companies have implemented computer-based information systems for recording sales transaction data. In the implementation of information systems, the data collected in the database is processed limited to making reports such as sales reports and inventory reports. Database generated from computer-based information systems can be further processed to obtain more valuable information. One strategy for using sales transaction data is to analyze consumer spending patterns. Consumer spending patterns can be in the form of associations of items that are often purchased simultaneously. The association between goods can be determined using the frequent itemset search technique. The Fp-growth algorithm is an algorithm that can be used to determine frequent itemsets in a data set. This article describes the results of implementing the FP-Growth algorithm in the consumer shopping pattern analysis application. The resulting shopping pattern is in the form of goods that are often purchased simultaneously by consumers. From the results of the application of the fp-growth algorithm, it was found that the minimum value of support had an effect, namely the smaller the input value of support, the more pairs of items were obtained. The application of the FP-Growth algorithm in determining frequent itemsets in association data mining can find customer spending habits in buying goods simultaneously.

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.

In the mining of association rules, the generation of frequent itemsets is a key factor that influence the efficiency and performance of the algorithm. With the increase of data dimension, it is obvious that the traditional association rules mining algorithm can’t meet the demand of high dimensional data mining. On the basis of Apriori algorithm, we put forward Split Mtrix _Apriori algorithm in this paper. By generating the Boolean matrix of the database, Split Mtrix _Apriori algorithm decreased the times of scanning database when generating the frequent itemsets. With adopting grouping processing strategy in the Boolean matrix, the algorithm can still keep high efficiency in dealing with high-dimensional data.So Split Mtrix _Apriori improved the efficiency of association rule mining significantly.

Computers have significantly impacted various fields, but managing vast amounts of information remains challenging. Artificial intelligence helps machines make data-driven decisions, but large datasets still pose difficulties for researchers. To tackle challenges and gain deep insights from large datasets, we proposed a novel technique using Geometric Progression series numbers (GPLN) for labeling singleton frequent itemsets and Cumulative Geometric Progression series numbers (CGPLN) for labeling itemsets with multiple frequent items. Initially, the algorithm used 2 as the constant 'r' for generating the series, but that was inadequate for large datasets. So, this paper proposes the Jagged Itemset Counting (JIC) algorithms 1 and 2 by reducing the value of ‘r’ and introduced dotted pairs for CGPLN labels to represent frequent itemsets. This redefined methodology requiring two passes over the transaction database: the first pass involves pre-processing, identifying singleton (1-k) frequent itemsets, determining GPLN, and partitioning the dataset. The partitions are processed sequentially: JIC-Algorithm-1 is applied to the first partition and JIC-Algorithm-2 to the remaining partitions. The n-k frequent itemsets from all partitions are combined using the Join algorithm, alongside 1-k itemsets found earlier. For small and medium-sized data, the JIC methodology outperforms Apriori and Eclat algorithms, showing better execution time even at low support thresholds. In Big Data scenarios, while FP-Growth and Eclat struggled, the proposed methodology excelled in execution time, main memory consumption, and disk memory utilization.

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.

In this paper, we present an improved method for predicting missing attribute values in data sets. We make use of frequent itemsets, generated from the association rules algorithm, displaying the correlations between different items in a set of transactions. In particular, we consider a database as a set of transactions and each data instance as an itemset. Then frequent itemsets can be used as a knowledge base to predict missing attribute values. Our approach integrates the RSFit method based on rough sets theory that produces faster predictions by considering similarities of attribute value pairs, but only for those attributes contained in the core or reduct of the data set. Using empirical studies on UCI and other real world data sets, we demonstrate a significant increase in prediction accuracy obtained from our new integrated approach, referred to as ItemRSFit.

The transaction database contains a very large and irregular dataset that requires another mechanism to read it, even though there is a lot of new knowledge that can be revealed, including associations or relationships between goods or products that are often purchased by customers. The new finding of the relationship between these variables is usually called association rule mining. The algorithm that is developing and often used is frequent pattern-growth (FP-Growth). The problem of very many transaction databases also occurred in Mr. A. So, in this research, we will look for customer patterns using the FP-Growth algorithm. The algorithm aims to find the maximum frequent itemset. The frequent itemset will be generated into associative rules so that it becomes valuable new knowledge. This knowledge can be used as a reference and consideration in making decisions. The FP-Growth algorithm will be implemented using the rapidminer tools on the transaction data of Mr.A's goods sales. The pattern of rules that will be searched for is based on data on sales of goods transactions. The results of the study obtained six association rules with five conclusions being the gift category. So that the suggestion for decision making is to lay out items close to and around the gift category in order to improve marketing and service strategies in order to attract the attention and interest of pointers in making purchases of goods.

A cosmetic cheerful shop located in the Marelan area of Medan city is a shop that sells women's cosmetic equipment. The owner of a cheerful cosmetics shop is very disciplined in recording sales and purchases of goods. Every transaction is always recorded and stored in the Microsoft Excel application. Meanwhile, the Ceria Cosmetics store has been running since 2015 and has recorded lots of transactions, both sales and purchases. From 2015 until now the data has been increasing, and so far this data has only been a record and stored in a folder on the store's computer. From the piles of transaction data at the cheerful cosmetics store, the authors took the idea of research to utilize this pile of data. One of the applications of information technology that can be used to utilize the pile of data is the data mining technique. By using data mining, a pile of data from a cheerful cosmetic store can be used to find association patterns. And the application of data mining with association rules can use the FP-Growth algorithm. The FP-Growth algorithm can be used as an alternative to determine the most frequently occurring data set (frequent itemset) in a data set. Later, the combination patterns obtained in this study can be used to arrange the layout of goods (cosmetics) at the Ceria Cosmetics store in Medan Marelan.

Finding the frequent itemset is one of the most investigated extents of data mining. The Apriori algorithm is the most established algorithm for frequent itemset mining, but it has issues regarding scanning frequent databases and generating a large amount of candidate sets. To solve these issues, an Improved Apriori algorithm was proposed. We examined the data structure, implementation, and algorithmic features that mainly focus on frequent itemset mining. We are representing an Improved Apriori algorithm on Spark in which simple and scalable implementation is done to achieve a faster process with lower support thresholds. We examined the improved Apriori algorithm on Extended Bakery Dataset and Retail Dataset. The results show execution time was reduced by 40% and 57% compared with the original Apriori algorithm.

The discovery of frequent itemsets is one of the very important topics in data mining. Frequent itemset discovery techniques help in generating qualitative knowledge which gives business insight and helps the decision makers. In the Big Data era the need for a customizable algorithm to work with big data sets in a reasonable time becomes a necessity. In this paper we propose a new algorithm for frequent itemset discovery that could work in distributed manner with big datasets. Our approach is based on the original Buddy Prima algorithm and the Greatest Common Divisor (GCD) calculation between itemsets which exist in the transaction database. The proposed algorithm introduces a new method to parallelize the frequent itemset mining without the need to generate candidate itemsets and also it avoids any communication overhead between the participated nodes. It explores the parallelism abilities in the hardware in case of single node operation. The proposed approach could be implemented using map-reduce technique or Spark. It was successfully applied on different size transactions DBs and compared with two well-known algorithms: FP-Growth and Parallel Apriori with different support levels. The experiments showed that the proposed algorithm achieves major time improvement over both algorithms especially with datasets having huge number of items.