Information theoretic clustering
Gokcay, E.
Principe, J.C.
Computational NeuroBiology Lab, Salk Inst., La Jolla, CA;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: Feb 2002
Volume: 24,
Issue: 2
On page(s): 158-171
ISSN: 0162-8828
References Cited: 51
CODEN: ITPIDJ
INSPEC Accession Number: 7187258
Digital Object Identifier: 10.1109/34.982897
Current Version Published: 2002-08-07
Abstract
Clustering is an important topic in pattern recognition. Since
only the structure of the data dictates the grouping (unsupervised
learning), information theory is an obvious criteria to establish the
clustering rule. The paper describes a novel valley seeking clustering
algorithm using an information theoretic measure to estimate the cost of
partitioning the data set. The information theoretic criteria developed
here evolved from a Renyi entropy estimator (A. Renyi, 1960) that was
proposed recently and has been successfully applied to other machine
learning applications (J.C. Principe et al., 2000). An improved version
of the k-change algorithm is used in optimization because of the
stepwise nature of the cost function and existence of local minima. Even
when applied to nonlinearly separable data, the new algorithm performs
well, and was able to find nonlinear boundaries between clusters. The
algorithm is also applied to the segmentation of magnetic resonance
imaging data (MRI) with very promising results
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