Abstract
Abstract Transactions with quantitative values are commonly seen in real-world applications. Fuzzy mining algorithms have thus been developed recently to induce linguistic knowledge from quantitative databases. In fuzzy data mining, the membership functions have a critical influence on the final mining results. How to effectively decide the membership functions in fuzzy data mining thus becomes very important. In the past, we proposed a fuzzy mining approach based on the Multi-Objective Genetic Algorithm (MOGA) to find the Pareto front of the desired membership functions. In this paper, we adopt a more sophisticated multi-objective approach, the SPEA2, to find the appropriate sets of membership functions for fuzzy data mining. Two objective functions are used to find the Pareto front. The first one is the suitability of membership functions and the second one is the total number of large 1-itemsets derived. Experimental comparisons of the proposed and the previous approaches are also made to show the effe...
Highlights
Data mining is the process of extracting desirable knowledge or interesting patterns from existing databases for specific purposes [6]
The number of large itemsets and the spent execution time were considered as two objective functions to derive appropriate membership functions for mining fuzzy association rules
We proposed a fuzzy mining approach based on the Multi-Objective Genetic Algorithm (MOGA) to find the Pareto front of the desired membership functions [9]
Summary
Data mining is the process of extracting desirable knowledge or interesting patterns from existing databases for specific purposes [6]. The number of large itemsets and the spent execution time were considered as two objective functions to derive appropriate membership functions for mining fuzzy association rules. Kaya proposed an approach based on multi-objective genetic algorithms for mining optimized fuzzy association rules [26]. He defined three objectives, namely strongness, interestingness and comprehensibility, to derive appropriate membership functions for mining optimized fuzzy association rules. We proposed a fuzzy mining approach based on the Multi-Objective Genetic Algorithm (MOGA) to find the Pareto front of the desired membership functions [9]. We adopt a more sophisticated multiobjective approach, the SPEA2 [35], to find the appropriate sets of membership functions for fuzzy data mining.
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