JMLR: A Unified Framework for Model-based Clustering

[[Redistributed from JMLR announce]]

~From: "David 'Pablo' Cohn" <[EMAIL PROTECTED]>
~Date: 14 Nov 2003 16:38:11 -0800
~Subject: jmlr-announce: A Unified Framework for Model-based Clustering

The Journal of Machine Learning Research (www.jmlr.org) is pleased to
announce publication of a new paper:
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A Unified Framework for Model-based Clustering
Shi Zhong, Joydeep Ghosh
JMLR 4(Nov):1001-1037, 2003

Abstract

Model-based clustering techniques have been widely used and have shown
promising results in many applications involving complex data. This
paper presents a unified framework for probabilistic model-based
clustering based on a bipartite graph view of data and models that
highlights the commonalities and differences among existing model-based
clustering algorithms. In this view, clusters are represented as
probabilistic models in a model space that is conceptually separate from
the data space. For partitional clustering, the view is conceptually
similar to the Expectation-Maximization (EM) algorithm. For hierarchical
clustering, the graph-based view helps to visualize critical/important
distinctions between similarity-based approaches and model-based
approaches. The framework also suggests several useful variations of
existing clustering algorithms. Two new variations---balanced
model-based clustering and hybrid model-based clustering---are discussed
and empirically evaluated on a variety of data types.

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This paper, and all previous papers in Volume 4 are available
electronically at http://www.jmlr.org in PostScript and PDF formats. The
papers of Volumes 1, 2 and 3 are also available electronically from the
JMLR website, and in hardcopy from the MIT Press; please see
http://mitpress.mit.edu/JMLR for details.

-David Cohn, <[EMAIL PROTECTED]>

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