MOGA for multi-level fuzzy data mining

A two-stage multi-objective genetic-fuzzy mining algorithm

“…Then, Kaya et al proposed another MOGA-based automated clustering approach for deciding the number of fuzzy sets and the fuzzy association rules with the two objective functions that are the number of large itemsets and the gain of time [2]. In [6,8], they also proposed MOGA-based approach for mining membership functions and fuzzy association rules with the given taxonomy and two objective functions that are the suitability of membership functions and the number of large itemsets. In the following, the descriptions of them are stated:…”

“…Chen et al also proposed a MOGA-based approach for fuzzy rule mining with two objective functions that are number of large itemset and suitability of membership functions [5]. Based on [7], MOGA-based approach with a given taxonomy is then proposed for mining multi-level fuzzy rules [8].…”

“…Usually, a larger number of 1-itemsets will result in a larger number of all itemsets with a higher probability, which will thus usually imply more interesting association rules [8]. In addition, the aim of this objective could also be got a maximum profit (maximum number of large itemsets) within an interval of user specified minimum support values [2].…”

“…Many MOEA-based GFM approaches have also been proposed with diverse objective functions [2,6,8,16]. Kaya proposed a multi-objective genetic algorithm based approach for discovering these optimized rules with three objective functions that are strongness (or support), interestingness (or confidence) and comprehensibility (or average number of fuzzy sets in the rule) [16].…”

“…Many MOGA-based GFM approaches have also been proposed with diverse objective functions, e.g., number of large itemsets, number of rules, suitability of membership functions [2,6,8,16]. Although the derived non-dominated solutions could provide different chooses to users, they still meet the dilemma of how to take appropriate solution among them.…”