Using Fuzzy C-means to Discover Concept-drift Patterns for Membership Functions

Transactions on Fuzzy Sets and Systems

مقاله 4، دوره 1، شماره 2، بهمن 2022، صفحه 21-31   

نوع مقاله: Original Article

شناسه دیجیتال (DOI): 10.30495/tfss.2022.1958730.1030

نویسندگان

Tzung-Pei Hong^{* 1}؛ Chun-Hao Chen²؛ Yan-Kang Li³؛ Min-Thai Wu⁴

¹Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan.

²Department of Information and Finance Management, National Taipei University of Technology, Taipei, Taiwan.

³Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung, Taiwan.

⁴College of Computer Science and Engineering, Shandong University of Science and Technology, Shandong, China.

چکیده

‎People often change their minds at different times and at different places‎. ‎It is important and valuable to indicate concept-drift patterns in unexpected ways for shopping behaviours for commercial applications‎. ‎Research about concept drift has been growing in recent years‎. ‎Many algorithms dealt with concept-drift information and detected new market trends‎. ‎This paper proposes an approach based on fuzzy c-means (FCM) to mine the concept drift of fuzzy membership functions‎. ‎The proposed algorithm is subdivided into two stages‎. ‎In the first stage‎, ‎individual fuzzy membership functions are generated from different training databases by the proposed FCM-based approach‎. ‎Then‎, ‎the proposed algorithm will mine the concept-drift patterns from the sets of fuzzy membership functions in the second stage‎. ‎Experiments on simulated datasets were also conducted to show the effectiveness of the approach‎.

کلیدواژه‌ها

Concept drift؛ Data mining؛ Fuzzy c-means؛ Membership function

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