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Genetic-fuzzy mining (GFM) was proposed to train fuzzy membership functions, thus enhancing the final solution quality. However, it is quite time-consuming because of frequent database scans for fitness evaluation. In this paper, we propose a parallel algorithm with MapReduce architecture to further speed up the genetic-fuzzy mining process. In the proposed approach, the master processor randomly...
In this article, we have introduced some genetic-fuzzy data mining techniques and their classification. The concept of fuzzy sets is used to handle quantitative transactions and the process of genetic calculation is executed to find appropriate membership functions. The main contributions of this paper are that we first divided the genetic-fuzzy mining problems into four kinds according to the types...
In this article, we have introduced some genetic-fuzzy data mining techniques and their classification. The concept of fuzzy sets is used to handle quantitative transactions and the process of genetic calculation is executed to find appropriate membership functions. The main contributions of this paper are that we first divided the genetic-fuzzy mining problems into four kinds according to the types...
In this paper, we adopt a more sophisticated multi-objective approach, SPEA2, to find appropriate sets of membership functions for fuzzy data mining. Two objective functions are used to find the Pareto front. The first one is to minimize the suitability of membership functions and the second one is to maximize the total number of large 1-itemsets. An experimental comparison with the previous approach...
In this paper, a GA-based framework for finding membership functions suitable for fuzzy mining problems is proposed. Each individual represents a possible set of membership functions for the items and is divided into two parts, control genes and parametric genes. Control genes are encoded into binary strings and used to determine whether membership functions are active or not. Each set of membership...
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