Hypergraph models and algorithms for data-pattern-based clustering

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buir.contributor.authorAykanat, Cevdet
dc.citation.epage57en_US
dc.citation.issueNumber1en_US
dc.citation.spage29en_US
dc.citation.volumeNumber9en_US
dc.contributor.authorOzdal, M. M.en_US
dc.contributor.authorAykanat, Cevdeten_US
dc.date.accessioned2016-02-08T10:26:39Z
dc.date.available2016-02-08T10:26:39Zen_US
dc.date.issued2004en_US
dc.departmentDepartment of Computer Engineeringen_US
dc.description.abstractIn traditional approaches for clustering market basket type data, relations among transactions are modeled according to the items occurring in these transactions. However, an individual item might induce different relations in different contexts. Since such contexts might be captured by interesting patterns in the overall data, we represent each transaction as a set of patterns through modifying the conventional pattern semantics. By clustering the patterns in the dataset, we infer a clustering of the transactions represented this way. For this, we propose a novel hypergraph model to represent the relations among the patterns. Instead of a local measure that depends only on common items among patterns, we propose a global measure that is based on the cooccurences of these patterns in the overall data. The success of existing hypergraph partitioning based algorithms in other domains depends on sparsity of the hypergraph and explicit objective metrics. For this, we propose a two-phase clustering approach for the above hypergraph, which is expected to be dense. In the first phase, the vertices of the hypergraph are merged in a multilevel algorithm to obtain large number of high quality clusters. Here, we propose new quality metrics for merging decisions in hypergraph clustering specifically for this domain. In order to enable the use of existing metrics in the second phase, we introduce a vertex-to-cluster affinity concept to devise a method for constructing a sparse hypergraph based on the obtained clustering. The experiments we have performed show the effectiveness of the proposed framework.en_US
dc.identifier.doi10.1023/B:DAMI.0000026903.59233.2aen_US
dc.identifier.issn1384-5810
dc.identifier.issn1573-756X
dc.identifier.urihttp://hdl.handle.net/11693/24268en_US
dc.language.isoEnglishen_US
dc.publisherSpringeren_US
dc.relation.isversionofhttp://dx.doi.org/10.1023/B:DAMI.0000026903.59233.2aen_US
dc.source.titleData Mining and Knowledge Discoveryen_US
dc.subjectClusteringen_US
dc.subjectData Miningen_US
dc.subjectData Patternsen_US
dc.subjectHypergraph Modelsen_US
dc.subjectPattern Semanticsen_US
dc.subjectAlgorithmsen_US
dc.subjectComputer Simulationen_US
dc.subjectDigital Storageen_US
dc.subjectGraph Theoryen_US
dc.subjectGroup Technologyen_US
dc.subjectMathematical Modelsen_US
dc.subjectMetric Systemen_US
dc.subjectSemanticsen_US
dc.subjectSet Theoryen_US
dc.subjectDatabase Systemsen_US
dc.titleHypergraph models and algorithms for data-pattern-based clusteringen_US
dc.typeArticleen_US

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