Finding Active Membership Functions for Genetic-Fuzzy Data Mining
學年 104
學期 1
出版(發表)日期 2015-11-01
作品名稱 Finding Active Membership Functions for Genetic-Fuzzy Data Mining
作品名稱(其他語言)
著者 Chun-Hao Chen; Tzung-Pei Hong; Yeong-Chyi Lee; Vincent S. Tseng
單位
出版者
著錄名稱、卷期、頁數 International Journal of Information Technology & Decision Making 14(16), p.1215-1242
摘要 Since transactions may contain quantitative values, many approaches have been proposed to derive membership functions for mining fuzzy association rules using genetic algorithms (GAs), a process known as genetic-fuzzy data mining. However, existing approaches assume that the number of linguistic terms is predefined. Thus, this study proposes a genetic-fuzzy mining approach for extracting an appropriate number of linguistic terms and their membership functions used in fuzzy data mining for the given items. The proposed algorithm adjusts membership functions using GAs and then uses them to fuzzify the quantitative transactions. Each individual in the population represents a possible set of membership functions for the items and is divided into two parts, control genes (CGs) and parametric genes (PGs). CGs are encoded into binary strings and used to determine whether membership functions are active. Each set of membership functions for an item is encoded as PGs with real-number schema. In addition, seven fitness functions are proposed, each of which is used to evaluate the goodness of the obtained membership functions and used as the evolutionary criteria in GA. After the GA process terminates, a better set of association rules with a suitable set of membership functions is obtained. Experiments are made to show the effectiveness of the proposed approach.
關鍵字 Data mining;fuzzy sets;fuzzy association rules;genetic algorithms;membership functions
語言 en
ISSN
期刊性質 國內
收錄於 SCI
產學合作
通訊作者
審稿制度
國別 TWN
公開徵稿
出版型式 ,電子版
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/109573 )