Graph-based data clustering: Criteria and a customizable approach

Yu Qian, Kang Zhang, Jiannong Cao

Research output: Journal article publicationJournal articleAcademic researchpeer-review


A graph model is often used to represent complex relational information in data clustering. Although there have been several kinds of graph structures, many graph-based clustering methods use a sparse graph model. The structure and weight information of a sparse graph decide the clustering result. This paper introduces a set of parameters to describe the structure and weight properties of a sparse graph. A set of measurement criteria of clustering results is presented based on the parameters. The criteria can be extended to represent the user's requirements. Based on the criteria the paper proposes a customizable algorithm that can produce clustering results according to users' inputs. The preliminary experiments on the customizability show encouraging results.
Original languageEnglish
Pages (from-to)903-908
Number of pages6
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publication statusPublished - 1 Dec 2004

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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