Mining Drift of Fuzzy Membership Functions

被引：1
|
作者：
Hong, Tzung-Pei [1 ,2 ]
Wu, Min-Thai [1 ]
Li, Yan-Kang [1 ]
Chen, Chun-Hao [3 ]
机构：
[1] Natl Univ Kaohsiung, Dept Comp Sci & Informat Engn, Kaohsiung, Taiwan
[2] Natl Sun Yat Sen Univ, Dept Comp Sci & Engn, Kaohsiung, Taiwan
[3] Tamkang Univ, Dept Comp Sci & Informat Engn, New Taipei, Taiwan
来源：
INTELLIGENT INFORMATION AND DATABASE SYSTEMS, ACIIDS 2016, PT II | 2016年 / 9622卷
关键词：
Concept drift; Data mining; Fuzzy C-means; Membership functions; RULES;
D O I：
10.1007/978-3-662-49390-8_20
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, the fuzzy c-means (FCM) clustering approach is adopted to find concept drift of fuzzy membership functions. The proposed algorithm is divided into two stages. In the first stage, the FCM approach is used to find appropriate fuzzy membership functions at different periods or at different places. Then in the second stage, the proposed algorithm compares the results in the first stage to find different types of drift of fuzzy membership functions. Experiments are also made to show the performance of the proposed approach.
引用
收藏
页码：211 / 218
页数：8
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