Clicks: An effective algorithm for mining subspace clusters in categorical datasets

Mohammed J. Zaki*, Markus Peters, Ira Assent, Thomas Seidl

*Corresponding author af dette arbejde

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avisTidsskriftartikelForskningpeer review

54 Citationer (Scopus)

Abstract

We present a novel algorithm called Clicks, that finds clusters in categorical datasets based on a search for k-partite maximal cliques. Unlike previous methods, Clicks mines subspace clusters. It uses a selective vertical method to guarantee complete search. Clicks outperforms previous approaches by over an order of magnitude and scales better than any of the existing method for high-dimensional datasets. These results are demonstrated in a comprehensive performance study on real and synthetic datasets.

OriginalsprogEngelsk
TidsskriftData and Knowledge Engineering
Vol/bind60
Nummer1
Sider (fra-til)51-70
Antal sider20
ISSN0169-023X
DOI
StatusUdgivet - jan. 2007
Udgivet eksterntJa

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