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Data Mining and Knowledge Discovery, 9, 2957, 2004 c 2004 Kluwer Academic Publishers. Manufactured in The Netherlands.
 

Summary: Data Mining and Knowledge Discovery, 9, 2957, 2004
c 2004 Kluwer Academic Publishers. Manufactured in The Netherlands.
Hypergraph Models and Algorithms
for Data-Pattern-Based Clustering
MUHAMMET MUSTAFA OZDAL ozdal@uiuc.edu
Department of Computer Science, University of Illinois at Urbana-Champaign
CEVDET AYKANAT aykanat@cs.bilkent.edu.tr
Computer Engineering Department, Bilkent University
Editors: Fayyad, Mannila, Ramakrishnan
Received April 2, 2001; Revised February 7, 2003
Abstract. In 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

  

Source: Aykanat, Cevdet - Department of Computer Engineering, Bilkent University

 

Collections: Computer Technologies and Information Sciences