Genetic algorithm based framework for mining fuzzy association rules

Abstract

It is not an easy task to know a priori the most appropriate fuzzy sets that cover the domains of quantitative attributes for fuzzy association rules mining, simply because characteristics of quantitative data are in general unknown. Besides, it is unrealistic that the most appropriate fuzzy sets can always be provided by domain experts. Motivated by this, in this paper we propose an automated method for mining fuzzy association rules. For this purpose, we first present a genetic algorithm (GA) based clustering method that adjusts centroids of the clusters, which are to be handled later as midpoints of triangular membership functions. Next, we give a different method for generating the membership functions by using Clustering Using Representatives (CURE) clustering algorithm, which is known as one of the most efficient clustering algorithms described in the literature. Finally, we compared the proposed GA-based approach with other approaches from the literature. Experiments conducted on 100K transactions from the US census in the year 2000 show that the proposed method exhibits a good performance in terms of execution time and interesting fuzzy association rules.

Big data analytics: a survey

“…For the association rules problem, the apriori algorithm [21] is one of the most popular methods. Nevertheless, because it is computationally very expensive, later studies [32] have attempted to use different approaches to reducing the cost of the apriori algorithm, such as applying the genetic algorithm to this problem [33]. In addition to considering the relationships between the input data, if we also consider the sequence or time series of the input data, then it will be referred to as the sequential pattern mining problem [34].…”

A Novel Approach for Discovery Quantitative Fuzzy Multi-Level Association Rules Mining Using Genetic Algorithm

“…The fitness value determines the relevant power of an individual to remain and create offspring in the next production. In the next iteration (t+1) a new resident is designed on the foundation of the operations (2) and (3) [29].…”

Selecting the best measures to discover quantitative association rules

“…In this context, the discovery of association rules (AR) -and particularly of quantitative association rules (QAR) in this work-is a popular methodology that allows the discovery of significant and apparently hidden relations among variables that form databases [3,4,27,28].…”

“…Alternatively, the support and confidence have been combined with the interest to form fitness functions in some works [27,28]. Their main particularity lies on the use of genetic algorithms to mine fuzzy association rules.…”