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A hierarchical information theoretic technique for the discovery of non linear alternative clusterings (2010)
| Content Provider | CiteSeerX |
|---|---|
| Author | Dang, Xuan Hong Bailey, James |
| Description | 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 perspec-tives 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 par-ticular, we propose a novel algorithm to uncover an alterna-tive clustering that is distinctively different from an exist-ing, reference clustering. Our technique is information the-ory based and aims to ensure alternative clustering quality by maximizing the mutual information between clustering labels and data observations, whilst at the same time en-suring 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. |
| File Format | |
| Language | English |
| Publisher Date | 2010-01-01 |
| Publisher Institution | In Proc. of the Int’l Conf. on Knowledge Discovery and Data Mining (KDD’10 |
| Access Restriction | Open |
| Subject Keyword | Linear Scenario Data Observation Alternative Clustering Novel Algorithm Mutual Information Non Linear Shape Non-linear Scenario Technique Performance Complex Datasets Information The-ory Important Method Hierarchical Information Theoretic Technique Non Linearity Non Linear Alternative Clustering Alterna-tive Clustering Large Range Different Perspec-tives Current Algorithm Time En-suring Alternative Clustering Distinctiveness New Hypothesis Reference Clustering |
| Content Type | Text |
| Resource Type | Article |
