NDLI: A hierarchical information theoretic technique for the discovery of non linear alternative clusterings

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A hierarchical information theoretic technique for the discovery of non linear alternative clusterings

Content Provider	ACM Digital Library
Author	Dang, Xuan-Hong Bailey, James
Abstract	Discovery of alternative clusterings is an important method for exploring complex datasets. It provides the capability for the user to view clustering behaviour from different perspectives and thus explore new hypotheses. However, current algorithms for alternative clustering have focused mainly on linear scenarios and may not perform as desired for datasets containing clusters with non linear shapes. Our goal in this paper is to address this challenge of non linearity. In particular, we propose a novel algorithm to uncover an alternative clustering that is distinctively different from an existing, reference clustering. Our technique is information theory based and aims to ensure alternative clustering quality by maximizing the mutual information between clustering labels and data observations, whilst at the same time ensuring alternative clustering distinctiveness by minimizing the information sharing between the two clusterings. We perform experiments to assess our method against a large range of alternative clustering algorithms in the literature. We show our technique's performance is generally better for non-linear scenarios and furthermore, is highly competitive even for simpler, linear scenarios.
Starting Page	573
Ending Page	582
Page Count	10
File Format	PDF QT / MOV
ISBN	9781450300551
DOI	10.1145/1835804.1835878
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2010-07-25
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Alternative clustering Information theoretic learning Parzenwindow technique
Content Type	Video Text
Resource Type	Article

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