A GA-based approach for mining membership functions and concept-drift patterns
學年 103
學期 2
發表日期 2015-05-25
作品名稱 A GA-based approach for mining membership functions and concept-drift patterns
作品名稱（其他語言）
著者 C. H. Chen; Y. Li; T. P. Hong; Y. K. Li; E. H. C. Lu
作品所屬單位
出版者
會議名稱 The IEEE Congress on Evolutionary Computation
會議地點
摘要 Since customers' behaviors may change over time in real applications, algorithms that can be utilized to mine these drift patterns are needed. In this paper, we propose a GA-based approach for mining fuzzy concept-drift patterns. It consists of two phases. The first phase mines membership functions and the second one finds fuzzy concept-drift patterns. In the first phase, appropriate membership functions for items are derived by GA with a designed fitness function. Then, the derived membership functions are utilized to mine fuzzy concept-drift patterns in the second phase. Experiments on simulated datasets are also made to show the effectiveness of the proposed approach.
關鍵字 concept drift;data mining;fuzzy association rules;genetic algorithms;membership functions
語言 en
收錄於
會議性質 國際
校內研討會地點 無
研討會時間 20150525~20150528
通訊作者
國別 JPN
公開徵稿
出版型式
出處 Evolutionary Computation (CEC), 2015 IEEE, pp.2961-2965
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/106387 )