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In this paper, we propose an approach for mining both type-2 membership functions and fuzzy association rules. The 2-tuple linguistic representation is utilized to encode type-2 membership functions into chromosomes. After the genetic evolution, the tuned type-2 membership functions are used to induce fuzzy association rules. Experiments on a simulated dataset are made to show the merit of the proposed...
In this paper, we propose a two-stage multi-objective fuzzy mining algorithm for dealing with linguistic knowledge discovery. In the first stage, the multi-objective genetic algorithm is used to derive a set of non-dominated membership functions (Pareto solutions) with two objective functions. In the second stage, the clustering technique is utilized to find representative solutions from the Pareto...
In this paper, we propose a Multi-Objective Multi-Level Genetic-Fuzzy Mining (MOMLGFM) algorithm for mining a set of non-dominated membership functions for mining multi-level fuzzy association rules. It first encodes the membership functions of each item class (category) into a chromosome according to the given taxonomy. The two objective functions of each chromosome are then calculated. The first...
In this paper, we propose a multiple-level genetic-fuzzy mining algorithm for mining membership functions and fuzzy association rule on multiple-concept levels. It first encodes the membership functions of each item class (category) into a chromosome according to the given taxonomy. The fitness value of each individual is then evaluated by the summation of large 1-itemsets of each item in different...
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