Cluster validity through graph-based boundary analysis

Conference Publication ResearchOnline@JCU
Yang, Jianjua;Lee, Ickjai
Ickjai Lee
Abstract

Gaining confidence that a clustering algorithm has produced meaningful results and not an accident of its usually heuristic optimization is central to data mining. This is the issue of cluster validity. We propose here a method by which proximity graphs are used to effectively detect border points and measure the margin between clusters. With analysis of boundary situation, we design a framework and relevant working principles to evaluate the separation and compactness in the clustering results. The method can obtain an insight into the internal structure in clustering result.

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2004 International Conference on Information and Knowledge Engineering

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978-1-932415-27-8

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Pages Count

5

Location

Nevada, USA

Publisher

CSREA Press

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Publisher Location

USA

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