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

‎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.

Keywords:

concept drift , Data mining , fuzzy c-means , Membership function