paper announcement

[jb@informatik.uni-bonn.de]

The following papers are available by anonymous ftp:

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FTP-host: atlas.cs.uni-bonn.de (131.220.10.29)
FTP-file: pub/papers/hofmann.nips94.ps.gz
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	Multidimensional Scaling and Data Clustering

	T. Hofmann  and J. Buhmann
	Rheinische Friedrich--Wilhelms--Universitaet
	Institut fuer Informatik III
	Roemerstrasse 164
	D-53117 Bonn, Germany

Abstract:
Visualizing and structuring pairwise dissimilarity data are difficult
combinatorial optimization problems known as "multidimensional scaling"
or "pairwise data clustering. Algorithms for embedding dissimilarity
data set in a Euclidian space, for clustering these data and for
actively selecting data to support the clustering process are
discussed in the maximum entropy framework. Active data selection 

provides a strategy to discover structure in a data set efficiently 

with partially unknown data.

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FTP-host: atlas.cs.uni-bonn.de (131.220.10.29)
FTP-file: pub/papers/buhmann.icpr94.ps.gz
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	A Maximum Entropy Approach to Pairwise Data Clustering

Abstract:
Partitioning a set of data points which are characterized by their
mutual dissimilarities instead of an explicit coordinate
representation is a difficult, NP-hard combinatorial
optimization problem. We formulate this optimization problem of a
pairwise clustering cost function in the maximum entropy framework
using a variational principle to derive corresponding data
partitionings in a d-dimensional Euclidian space. This approximation
solves the embedding problem and the grouping of these data into clusters
simultaneously and in a selfconsistent fashion.