Moulavi, Davoud, Jaskowiak, Pablo A., Campello, Ricardo J.G.B., Zimek, Arthur, and Sander, Jörg (2014) Density-based clustering validation. In: Proceedings of the SIAM International Conference on Data Mining. pp. 839-847. From: SM2014: SIAM International Conference on Data Mining, 24-26 April 2014, Philadelphia, PA, USA.

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Abstract

One of the most challenging aspects of clustering is validation, which is the objective and quantitative assessment of clustering results. A number of different relative validity criteria have been proposed for the validation of globular, clusters. Not all data, however, are composed of globular clusters. Density-based clustering algorithms seek partitions with high density areas of points (clusters, not necessarily globular) separated by low density areas, possibly containing noise objects. In these cases relative validity indices proposed for globular cluster validation may fail. In this paper we propose a relative validation index for density-based, arbitrarily shaped clusters. The index assesses clustering quality based on the relative density connection between pairs of objects. Our index is formulated on the basis of a new kernel density function, which is used to compute the density of objects and to evaluate the within- and between-cluster density connectedness of clustering results. Experiments on synthetic and real world data show the effectiveness of our approach for the evaluation and selection of clustering algo rithms and their respective appropriate parameters.

Item ID:	46783
Item Type:	Conference Item (Research - E1)
ISBN:	978-1-61197-344-0
Date Deposited:	14 Mar 2017 01:21
FoR Codes:	01 MATHEMATICAL SCIENCES > 0104 Statistics > 010401 Applied Statistics @ 100%
SEO Codes:	97 EXPANDING KNOWLEDGE > 970101 Expanding Knowledge in the Mathematical Sciences @ 100%
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