Abstract

Noise fuzzy clustering is a useful scheme for analyzing intrinsic data structures through robust estimation of fuzzy c-partition. In this paper, a novel noise rejection scheme for improving fuzzy co-clustering is proposed, which is useful in such cooccurrence information analysis as document classification, where multi-topic co-cluster structure extraction by probabilistic latent semantic analysis (pLSA) is achieved through rejection of the influences of noise objects. Supported by the uniform noise distribution concept in noise fuzzy clustering, a noise cluster having uniform item occurrence probabilities is newly introduced into the pLSA-induced fuzzy co-clustering model. Several numerical experiments demonstrate the advantage of tuning the noise sensitivity of the pLSA-induced objective function.

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