A survey on association rules mining using heuristics

Abstract

Association rule mining (ARM) is a commonly encountred data mining method. There are many approaches to mining frequent rules and patterns from a database and one among them is heuristics. Many heuristic approaches have been proposed but, to the best of our knowledge, there is no comprehensive literature review on such approaches, yet with only a limited attempt. This gap needs to be filled. This paper reviews heuristic approaches to ARM and points out their most significant strengths and weaknesses. We propose eight performance metrics, such as execution time, memory consumption, completeness, and interestingness, we compare approaches against these performance metrics and discuss our findings. For instance, comparison results indicate that SRmining, PMES, Ant‐ARM, and MDS‐H are the fastest heuristic ARM algorithms. HSBO‐TS is the most complete one, while SRmining and ACS require only one database scan. In addition, we propose a parameter, named GT‐Rank for ranking heuristic ARM approaches, and based on that, ARMGA, ASC, and Kua emerge as the best approaches. We also consider ARM algorithms and their characteristics as transactions and items in a transactional database, respectively, and generate association rules that indicate research trends in this area. This article is categorized under: Algorithmic Development > Association Rules Technologies > Association Rules Fundamental Concepts of Data and Knowledge > Motivation and Emergence of Data Mining

Association Rules Mining

“…It should be noted that, to our knowledge, the only comparison between the previous methods and APRIORI has been carried out in Hamdad et al [30] which show that GA-ARM have given the best results in terms of number of obtained rules when compared with AGA, IGA, ARMGA, MBAREA on three datasets: IBM Quest synthetic database, Mushroom and Chess [28]; UCI 2015). A recent survey have been proposed on heuristics in association rules mining (see [24]).…”

“…But these surveys had different objectives. Indeed, in 2020, Ghafari et al [24] focused on works addressing ARs using heuristics. Algorithms such as ARGMA, HBSO and others have been compared according to several metrics as execution time, relevance, memory used, etc.…”

Data Mining Algorithms for Smart Cities: A Bibliometric Analysis

“…Association Rules (AR) is one of the most popular methods [125] within the context of extracting relationships among items hidden within data sets [126], as it has been used in several smart city applications [44,77,127,128]. Agrawal and Strikant [129] presented the Apriori algorithm for discovering all significant AR between items in a large database of transactions.…”

Whale Optimization Algorithm For Solving Association Rule Mining Issue

“…In this proposal, the authors modified the cuckoo search algorithm to a binary algorithm based on the sigmoid function and applied it to the ARM issue. A fresh survey outlined the whole domain based heuristic methods is in [31] .…”