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Learning multiple nonredundant clusterings

Learning multiple nonredundant clusterings Learning Multiple Nonredundant Clusterings YING CUI Yahoo! Labs XIAOLI Z. FERN Oregon State University and JENNIFER G. DY Northeastern University Real-world applications often involve complex data that can be interpreted in many different ways. When clustering such data, there may exist multiple groupings that are reasonable and interesting from different perspectives. This is especially true for high-dimensional data, where different feature subspaces may reveal different structures of the data. However, traditional clustering is restricted to nding only one single clustering of the data. In this article, we propose a new clustering paradigm for exploratory data analysis: nd all non-redundant clustering solutions of the data, where data points in the same cluster in one solution can belong to different clusters in other partitioning solutions. We present a framework to solve this problem and suggest two approaches within this framework: (1) orthogonal clustering, and (2) clustering in orthogonal subspaces. In essence, both approaches nd alternative ways to partition the data by projecting it to a space that is orthogonal to the current solution. The rst approach seeks orthogonality in the cluster space, while the second approach seeks orthogonality in the feature space. We study the relationship between the two approaches. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Knowledge Discovery from Data (TKDD) Association for Computing Machinery

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2010 by ACM Inc.
ISSN
1556-4681
DOI
10.1145/1839490.1839496
Publisher site
See Article on Publisher Site

Abstract

Learning Multiple Nonredundant Clusterings YING CUI Yahoo! Labs XIAOLI Z. FERN Oregon State University and JENNIFER G. DY Northeastern University Real-world applications often involve complex data that can be interpreted in many different ways. When clustering such data, there may exist multiple groupings that are reasonable and interesting from different perspectives. This is especially true for high-dimensional data, where different feature subspaces may reveal different structures of the data. However, traditional clustering is restricted to nding only one single clustering of the data. In this article, we propose a new clustering paradigm for exploratory data analysis: nd all non-redundant clustering solutions of the data, where data points in the same cluster in one solution can belong to different clusters in other partitioning solutions. We present a framework to solve this problem and suggest two approaches within this framework: (1) orthogonal clustering, and (2) clustering in orthogonal subspaces. In essence, both approaches nd alternative ways to partition the data by projecting it to a space that is orthogonal to the current solution. The rst approach seeks orthogonality in the cluster space, while the second approach seeks orthogonality in the feature space. We study the relationship between the two approaches.

Journal

ACM Transactions on Knowledge Discovery from Data (TKDD)Association for Computing Machinery

Published: Oct 1, 2010

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