Improving performance of similarity-based clustering by featureweight learning
Yeung, D.S.
Wang, X.Z.
Dept. of Comput., Hong Kong Polytech. Univ., Kowloon;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: Apr 2002
Volume: 24,
Issue: 4
On page(s): 556-561
ISSN: 0162-8828
References Cited: 16
CODEN: ITPIDJ
INSPEC Accession Number: 7241614
Digital Object Identifier: 10.1109/34.993562
Current Version Published: 2002-08-07
Abstract
Similarity-based clustering is a simple but powerful technique
which usually results in a clustering graph for a partitioning of
threshold values in the unit interval. The guiding principle of
similarity-based clustering is "similar objects are grouped in the same
cluster." To judge whether two objects are similar, a similarity measure
must be given in advance. The similarity measure presented in the paper
is determined in terms of the weighted distance between the features of
the objects. Thus, the clustering graph and its performance (which is
described by several evaluation indices defined in the paper) will
depend on the feature weights. The paper shows that, by using gradient
descent technique to learn the feature weights, the clustering
performance can be significantly improved. It is also shown that our
method helps to reduce the uncertainty (fuzziness and nonspecificity) of
the similarity matrix. This enhances the quality of the similarity-based
decision making
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