On weighting clustering

Recent papers and patents in iterative unsupervised learning have emphasized a new trend in clustering. It basically consists of penalizing solutions via weights on the instance points, somehow making clustering move toward the hardest points to cluster. The motivations come principally from an anal...

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Bibliographic Details
Published in:IEEE transactions on pattern analysis and machine intelligence Vol. 28; no. 8; pp. 1223 - 1235
Main Authors: Nock, R., Nielsen, F.
Format: Journal Article
Language:English
Published: Los Alamitos, CA IEEE 01.08.2006
IEEE Computer Society
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithm design and analysis
Algorithms
Analogies
Applied sciences
Artificial Intelligence
Boosting
Bregman divergences
Cluster Analysis
Clustering
Clustering algorithms
Clusters
Computer science; control theory; systems
Computer Simulation
Design methodology
Displays
Exact sciences and technology
expectation maximization
Fuzzy
fuzzy k\hbox{-}\rm means
harmonic means clustering
Information Storage and Retrieval - methods
Intelligence
Iterative algorithms
k\hbox{-}\rm means
Learning and adaptive systems
Maximization
Minimization methods
Models, Statistical
Pattern analysis
Pattern Recognition, Automated - methods
Studies
Tail
Taylor series
Unsupervised learning
Fuzzy logic
fuzzy k-means
Bregman divergences
harmonic means clustering
k-means
K means algorithm
expectation maximization
Clustering
EM algorithm
Pattern analysis
Unsupervised learning
Constrained optimization
ISSN:0162-8828, 2160-9292, 1939-3539
Online Access:Get full text
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Summary:Recent papers and patents in iterative unsupervised learning have emphasized a new trend in clustering. It basically consists of penalizing solutions via weights on the instance points, somehow making clustering move toward the hardest points to cluster. The motivations come principally from an analogy with powerful supervised classification methods known as boosting algorithms. However, interest in this analogy has so far been mainly borne out from experimental studies only. This paper is, to the best of our knowledge, the first attempt at its formalization. More precisely, we handle clustering as a constrained minimization of a Bregman divergence. Weight modifications rely on the local variations of the expected complete log-likelihoods. Theoretical results show benefits resembling those of boosting algorithms and bring modified (weighted) versions of clustering algorithms such as k-means, fuzzy c-means, expectation maximization (EM), and k-harmonic means. Experiments are provided for all these algorithms, with a readily available code. They display the advantages that subtle data reweighting may bring to clustering
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ISSN:0162-8828
2160-9292
1939-3539
DOI:10.1109/TPAMI.2006.168