Fuzzy Data Mining for Segmentation and Profiling

The paper presents fuzzy data mining for churn behavior analysis in telecom. Fuzzy data mining is used to identify the clusters in the given large dataset using the modified single pass fuzzy clustering algorithm. Subsequently, fuzzy rule induction is used to reveal variables that provide maximal separation between churners and non-churners in different clusters. This study provides evidence against extra resources for handling large data. The preliminary results are encouraging. The quality of clusters and rules derived for classification are almost identical on both the proposed approach and the original Fuzzy C-Means algorithm.