Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/27646
Title: Graph-based data clustering : criteria and a customizable approach
Authors: Qian, Y
Zhang, K
Cao, J 
Issue Date: 2004
Publisher: Springer
Source: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics), 2004, v. 2690, p. 903-908 How to cite?
Journal: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics) 
Abstract: 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.
URI: http://hdl.handle.net/10397/27646
ISSN: 0302-9743
EISSN: 1611-3349
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