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Data clustering: a review

Jain, A ; Murty, M ; Flynn, P

ACM computing surveys, 1999-09, Vol.31 (3), p.264-323 [Periódico revisado por pares]

New York, NY: ACM

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Título:
Data clustering: a review
Autor: Jain, A ; Murty, M ; Flynn, P
Assuntos: Applied sciences ; Artificial intelligence ; Cluster analysis ; Clustering ; clustering applications ; Computer science; control theory; systems ; Exact sciences and technology ; exploratory data analysis ; incremental clustering ; Information management ; Methods ; Pattern recognition ; Pattern recognition. Digital image processing. Computational geometry ; similarity indices ; Studies ; unsupervised learning
É parte de: ACM computing surveys, 1999-09, Vol.31 (3), p.264-323
Descrição: Clustering is the unsupervised classification of patterns (observations, data items, or feature vectors) into groups (clusters). The clustering problem has been addressed in many contexts and by researchers in many disciplines; this reflects its broad appeal and usefulness as one of the steps in exploratory data analysis. However, clustering is a difficult problem combinatorially, and differences in assumptions and contexts in different communities has made the transfer of useful generic concepts and methodologies slow to occur. This paper presents an overviewof pattern clustering methods from a statistical pattern recognition perspective, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners. We present a taxonomy of clustering techniques, and identify cross-cutting themes and recent advances. We also describe some important applications of clustering algorithms such as image segmentation, object recognition, and information retrieval.
Editor: New York, NY: ACM
Idioma: Inglês

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Data clustering: a review

Jain, A ; Murty, M ; Flynn, P

New York, NY: ACM

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