IFD: Iterative feature and data clustering

Abstract

In this paper, we propose a new clustering algorithm, IFD1, based on a cluster model of data coefficients D and feature coefficients F, The coefficients denote the degree (or weights) of the data and features associated with the clusters. Clustering is performed via an iterative optimization procedure to mutually reinforce the relationships between the coefficients. The mutually reinforcing optimization exploits the duality of the data and features and enable a simultaneous clustering of both data and features. We have shown the convergence property of the clustering algorithm and discussed its connections with various existential approaches. Extensive experimental results on both synthetic and real data sets : show the effectiveness of IFD algorithm.