<rdf:RDF xmlns:community="http://www.bibsonomy.org/ontologies/2008/05/community#" xmlns:foaf="http://xmlns.com/foaf/0.1/" xmlns:owl="http://www.w3.org/2002/07/owl#" xmlns:admin="http://webns.net/mvcb/" xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:syn="http://purl.org/rss/1.0/modules/syndication/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:taxo="http://purl.org/rss/1.0/modules/taxonomy/" xmlns:cc="http://web.resource.org/cc/" xmlns:xsd="http://www.w3.org/2001/XMLSchema#" xmlns:swrc="http://swrc.ontoware.org/ontology#" xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#" xmlns="http://purl.org/rss/1.0/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xml:base="http://www.bibsonomy.org/user/grani/neural"><owl:Ontology rdf:about=""><rdfs:comment>BibSonomy publications for /user/grani/neural</rdfs:comment><owl:imports rdf:resource="http://swrc.ontoware.org/ontology/portal"/></owl:Ontology><rdf:Description rdf:about="http://www.bibsonomy.org/bibtex/292c03ba02a41f95ae315273939c8daa5/grani"><owl:sameAs rdf:resource="http://www.bibsonomy.org/uri/bibtex/292c03ba02a41f95ae315273939c8daa5/grani"/><rdf:type rdf:resource="http://swrc.ontoware.org/ontology#Article"/><swrc:date>Sun Jul 16 10:28:56 CEST 2006</swrc:date><swrc:journal>Neural Networks, IEEE Transactions on</swrc:journal><swrc:number>3</swrc:number><swrc:pages>645--678</swrc:pages><swrc:title>Survey of clustering algorithms</swrc:title><swrc:volume>16</swrc:volume><swrc:year>2005</swrc:year><swrc:keywords>clustering, algorithm, cluster bioinformatics, classification, Adaptive benchmark theory feature traveling problem, salesman analysis, self-organizing sets, resonance clustering validation, data neural networks, (ART), map proximity, pattern (SOFM) </swrc:keywords><swrc:abstract>Data analysis plays an indispensable role for understanding various
	phenomena. Cluster analysis, primitive exploration with little or
	no prior knowledge, consists of research developed across a wide
	variety of communities. The diversity, on one hand, equips us with
	many tools. On the other hand, the profusion of options causes confusion.
	We survey clustering algorithms for data sets appearing in statistics,
	computer science, and machine learning, and illustrate their applications
	in some benchmark data sets, the traveling salesman problem, and
	bioinformatics, a new field attracting intensive efforts. Several
	tightly related topics, proximity measure, and cluster validation,
	are also discussed.</swrc:abstract><swrc:hasExtraField><swrc:Field swrc:value="2006.06.08" swrc:key="timestamp"/></swrc:hasExtraField><swrc:hasExtraField><swrc:Field swrc:value="1045-9227" swrc:key="issn"/></swrc:hasExtraField><swrc:hasExtraField><swrc:Field swrc:value="mgrani" swrc:key="owner"/></swrc:hasExtraField><swrc:author><rdf:Seq><rdf:_1><swrc:Person swrc:name="Rui Xu"/></rdf:_1><rdf:_2><swrc:Person swrc:name="II Wunsch"/></rdf:_2></rdf:Seq></swrc:author></rdf:Description></rdf:RDF>