A NOTE ON WEIGHTED FUZZY K-MEANS CLUSTERING FOR CONCEPT DECOMPOSITION

While reducing the dimensionality of a corpus, concept decomposition (CD) based on fuzzy K-means (FKM) clustering provides better approximation than CD based on spherical k-means clustering. However, performance of the FKM algorithm is limited by its distance metric and it is proved that assignment of feature weights can improve the performance of FKM. Our work builds upon this analysis and proposes two approaches to feature weight selection. Using four testing document collections, we demonstrate that the CD based on the proposed feature-weighted FKM provides better approximation than the CD based on FKM while maintaining the quality of retrieval.