Multi-objective genetic algorithm based approaches for mining optimized fuzzy association rules

Abstract

Association rules form one of the most widely used techniques to discover correlations among attribute in a database. So far, some efficient methods have been proposed to obtain these rules with respect to an optimal goal, such as: to maximize the number of large itemsets and interesting rules or the values of support and confidence for the discovered rules. This paper first introduces optimized fuzzy association rule mining in terms of three important criteria; strongness, interestingness and comprehensibility. Then, it proposes multi-objective Genetic Algorithm (GA) based approaches for discovering these optimized rules. Optimization technique according to given criterion may be one of two different forms; The first tries to determine the appropriate fuzzy sets of quantitative attributes in a prespecified rule, which is also called as certain rule. The second deals with finding both uncertain rules and their appropriate fuzzy sets. Experimental results conducted on a real data set show the effectiveness and applicability of the proposed approach.

Evolving temporal fuzzy itemsets from quantitative data with a multi-objective evolutionary algorithm

“…This is a challenging task that involves simultaneously searching the itemset space, the temporal space and the quantitative space, which together form a multi-dimensional search space. Previous MOEAs for association rule mining have focused on Boolean data [23] and quantitative datasets [15], but not the composition of temporal and quantitative, which is a significant step in problem dimensionality.…”

“…This typically aims to tune the membership functions to produce maximum support for 1-itemsets before exhaustively mining the rules. Another approach is to extract the association rules as well as define the attribute intervals [13] or membership functions [15]. In [17], an alternative approach to tuning the fuzzy sets includes combining clustering with a MOEA.…”

“…They provide a linguistic interpretation of numerical values, which is of importance when interfacing with experts. Evolving fuzzy association rules [15] enhances the interpretability of quantitative association rules.…”

Evolving Temporal Fuzzy Association Rules from Quantitative Data with a Multi-Objective Evolutionary Algorithm

“…They provide a linguistic interpretation of numerical values for interfacing with experts. Evolving fuzzy association rules [9] enhances the interpretability of quantitative association rules.…”

“…Membership functions are tuned to produce maximum support for 1-itemsets before exhaustively mining rules. Another approach is to extract association rules whilst defining attribute intervals [8] or membership functions [9]. The latter approach is adopted in this paper.…”