A REVIEW OF ASSOCIATION RULE MINING TECHNIQUES TOWARD EFFICIENT FREQUENT PATTERN DISCOVERY

Abstract

The rapid growth of digital technologies and transactional systems has generated massive volumes of structured and unstructured data, creating a strong demand for efficient data mining techniques capable of extracting meaningful knowledge from large datasets. Association Rule Mining (ARM) is one of the most widely used data mining approaches for discovering hidden relationships, frequent patterns, and correlations among items in transactional databases. Traditional ARM algorithms such as Apriori, FP-Growth, and ECLAT have been extensively used for frequent itemset generation and rule discovery. However, these techniques still suffer from several limitations, including repeated database scans, excessive candidate generation, memory overhead, scalability challenges, and increased computational complexity when handling large-scale and sparse datasets.

This paper presents a comprehensive review of association rule mining techniques and critically examines the working principles, advantages, and limitations of major ARM algorithms. A comparative analysis of widely used approaches, including Apriori, FP-Growth, ECLAT, weighted association rule mining, and hybrid mining strategies, is discussed based on performance parameters such as execution time, database scanning requirements, memory consumption, and scalability. Furthermore, the paper highlights major research challenges associated with large transactional environments and discusses emerging research trends toward efficient and adaptive mining frameworks. The findings indicate that future developments in association rule mining may focus on improving mining efficiency through optimized and scalable hybrid strategies capable of handling complex real-world datasets.