Neighbourhood contrast : A better means to detect clusters than density

Chen, Bo; Ting, Kaiming

Title: Neighbourhood contrast : A better means to detect clusters than density
Creator: Chen, Bo; Ting, Kaiming
Date: 2018
Type: Text; Conference paper
Identifier: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/166069
Identifier: vital:13361
Identifier: https://doi.org/10.1007/978-3-319-93040-4_32
Identifier: ISBN:03029743 (ISSN); 9783319930398 (ISBN)
Abstract: Most density-based clustering algorithms suffer from large density variations among clusters. This paper proposes a new measure called Neighbourhood Contrast (NC) as a better alternative to density in detecting clusters. The proposed NC admits all local density maxima, regardless of their densities, to have similar NC values. Due to this unique property, NC is a better means to detect clusters in a dataset with large density variations among clusters. We provide two applications of NC. First, replacing density with NC in the current state-of-the-art clustering procedure DP leads to significantly improved clustering performance. Second, we devise a new clustering algorithm called Neighbourhood Contrast Clustering (NCC) which does not require density or distance calculations, and therefore has a linear time complexity in terms of dataset size. Our empirical evaluation shows that both NC-based methods outperform density-based methods including the current state-of-the-art.
Publisher: Springer Verlag
Relation: 22nd Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2018; Melbourne, Australia; 3rd-6th June 2018; published in Lecutre Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 10939 LNAI, p. 401-412
Rights: This metadata is freely available under a CCO license
Subject: Clustering; Neighbourhood contrast
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