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Description: 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 overview
of 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.-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 overview
of 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.
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Author: nadjia |
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