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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Veröffentlicht in:	IEEE transactions on pattern analysis and machine intelligence Jg. 28; H. 8; S. 1223 - 1235
Hauptverfasser:	Nock, R., Nielsen, F.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Los Alamitos, CA IEEE 01.08.2006 IEEE Computer Society The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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-Zugang:	Volltext
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