MOGA-based fuzzy data mining with taxonomy
學年 102
學期 1
出版（發表）日期 2013-12-01
作品名稱 MOGA-based fuzzy data mining with taxonomy
作品名稱（其他語言）
著者 Chen, Chun-Hao; He, Ji-Syuan; Hong, Tzung-Pei
單位 淡江大學資訊工程學系
出版者 Amsterdam: Elsevier BV
著錄名稱、卷期、頁數 Knowledge-Based Systems 54, pp.53-65
摘要 Transactions in real-world applications usually consist of quantitative values. Some fuzzy data mining approaches have thus been proposed for deriving linguistic rules from such transactions. Since membership functions may have a critical influence on the final mining results, several genetic-fuzzy mining approaches have been proposed for mining appropriate membership functions and fuzzy association rules at the same time. Most of them, however, focus on a single level and consider only one objective function. This paper proposes 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. The algorithm first encodes the membership functions of each item class (category) into a chromosome according to the given taxonomy. Two objective functions are then considered. The first one is the knowledge amount mined out at different levels, and the second one is the suitability of membership functions. The fitness value of each individual is then evaluated using these two objective functions. After the evolutionary process terminates, various sets of membership functions can be used for deriving multi-level fuzzy association rules according to decision-makers. Experimental results on the simulated and real datasets show the effectiveness of the proposed algorithm.
關鍵字 Data mining;Fuzzy sets;Fuzzy rules;Multi-objective genetic algorithm;Taxonomy
語言 en
ISSN 0950-7051
期刊性質 國外
收錄於 SCI
產學合作
通訊作者 Hong, Tzung-Pei
審稿制度 是
國別 NLD
公開徵稿
出版型式 ,紙本
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