PUMA publications for /user/jaeschke/reviewhttps://puma.uni-kassel.de/user/jaeschke/reviewPUMA RSS feed for /user/jaeschke/review2024-03-29T01:56:23+01:00Data clustering: a reviewhttps://puma.uni-kassel.de/bibtex/2b19bcef82a04eb82ee4abde53ee7d1c2/jaeschkejaeschke2009-03-11T13:11:44+01:00clustering data review <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="A. K. Jain" itemprop="url" href="/author/A.%20K.%20Jain"><span itemprop="name">A. Jain</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="M. N. Murty" itemprop="url" href="/author/M.%20N.%20Murty"><span itemprop="name">M. Murty</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="P. J. Flynn" itemprop="url" href="/author/P.%20J.%20Flynn"><span itemprop="name">P. Flynn</span></a></span>. </span><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><span itemtype="http://schema.org/Periodical" itemscope="itemscope" itemprop="isPartOf"><span itemprop="name"><em>ACM Comput. Surv.</em></span></span> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">31 </span></span>(<span itemprop="issueNumber">3</span>):
<span itemprop="pagination">264--323</span></em> </span>(<em><span>1999<meta content="1999" itemprop="datePublished"/></span></em>)Wed Mar 11 13:11:44 CET 2009New York, NY, USAACM Comput. Surv.3264--323Data clustering: a review311999clustering data review 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. 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.Data clustering