Density-based clustering based on hierarchical density estimates

Ricardo J.G.B. Campello, Davoud Moulavi, Joerg Sander

Publikation: Kapitel i bog/rapport/konference-proceedingKonferencebidrag i proceedingsForskningpeer review

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.

OriginalsprogEngelsk
TitelAdvances in Knowledge Discovery and Data Mining - 17th Pacific-Asia Conference, PAKDD 2013, Proceedings
RedaktørerJian Pei, Vincent Tseng, Longbing Cao, Hiroshi Motoda, Guandong Xu
Vol/bindPART 2
ForlagSpringer
Publikationsdato2013
Sider160-172
ISBN (Trykt)9783642374555
DOI
StatusUdgivet - 2013
Udgivet eksterntJa
Begivenhed17th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2013 - Gold Coast, QLD, Australien
Varighed: 14. apr. 201317. apr. 2013

Konference

Konference17th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2013
Land/OmrådeAustralien
ByGold Coast, QLD
Periode14/04/201317/04/2013
NavnLecture Notes in Computer Science
NummerPART 2
Vol/bind7819 LNAI
ISSN0302-9743

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