Density-based clustering based on hierarchical density estimates

Campello, Ricardo J.G.B., Moulavi, Davoud, and Sander, Joerg (2013) Density-based clustering based on hierarchical density estimates. In: Lecture Notes in Computer Science (7819), pp. 160-172. From: Pacific-Asia Conference on Knowledge Discovery and Data Mining, 14-17 April 2013, Gold Coast, QLD, Australia.

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Abstract

We propose a theoretically and practically improved density-based, hierarchical clustering method, providing a clustering hierarchy from which a simplified tree of significant clusters can be constructed. For obtaining a "flat" partition consisting of only the most significant clusters (possibly corresponding to different density thresholds), we propose a novel cluster stability measure, formalize the problem of maximizing the overall stability of selected clusters, and formulate an algorithm that computes an optimal solution to this problem. We demonstrate that our approach outperforms the current, state-of-the-art, density-based clustering methods on a wide variety of real world data.

Item ID: 46784
Item Type: Conference Item (Research - E1)
ISSN: 1611-3349
Date Deposited: 13 Jun 2017 02:44
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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