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.