教師資料查詢 | 類別: 期刊論文 | 教師: 陳俊豪 CHUN-HAO CHEN (瀏覽個人網頁)

標題: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
語言英文
ISSN
期刊性質國內
收錄於SCI;
產學合作
通訊作者
審稿制度
國別中華民國
公開徵稿
出版型式,電子版
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