Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/31884
Title: A parameter free approach for clustering analysis
Authors: Huang, H
Mok, PY 
Kwok, YL
Au, SC 
Issue Date: 2009
Publisher: Springer
Source: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics), 2009, v. 5702 lncs, p. 816-823 How to cite?
Journal: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics) 
Abstract: In the paper, we propose a novel parameter free approach for clustering analysis. The approach needs not to make assumptions or define parameters on the cluster number or the results, while the clustered results are visually verified and approved by experimental work. For simplicity, this paper demonstrates the idea using Fuzzy C-Means (FCMs) clustering method, but the proposed open framework allows easy integration with other clustering methods. The method-independent framework generates optimal clustering results and avoids intrinsic biases from individual clustering methods.
Description: 13th International Conference on Computer Analysis of Images and Patterns, CAIP 2009, Munster, 2-4 September 2009
URI: http://hdl.handle.net/10397/31884
ISBN: 3642037666
9783642037665
ISSN: 0302-9743
EISSN: 1611-3349
DOI: 10.1007/978-3-642-03767-2_99
Appears in Collections:Conference Paper

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