A Partitioning Based Algorithm to Fuzzy Tricluster

Abstract EN

Fuzzy clustering allows an object to exist in multiple clusters and represents the affiliation of objects to clusters by memberships.

It is extended to fuzzy coclustering by assigning both objects and features membership functions.

In this paper we propose a new fuzzy triclustering (FTC) algorithm for automatic categorization of three-dimensional data collections.

FTC specifies membership function for each dimension and is able to generate fuzzy clusters simultaneously on three dimensions.

Thus FTC divides a three-dimensional cube into many little blocks which should be triclusters with strong coherent bonding among its members.

The experimental studies on MovieLens demonstrate the strength of FTC in terms of accuracy compared to some recent popular fuzzy clustering and coclustering approaches.