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Enhancing Clustering Quality through Landmark-Based Dimensionality Reduction

Enhancing Clustering Quality through Landmark-Based Dimensionality Reduction TKD00016 ACM (Typeset by SPi, Manila, Philippines) 1 of 44 February 22, 2011 Enhancing Clustering Quality through Landmark-Based Dimensionality Reduction PANAGIS MAGDALINOS, Athens University of Economics and Business CHRISTOS DOULKERIDIS, Norwegian University of Science and Technology MICHALIS VAZIRGIANNIS, Athens University of Economics and Business Scaling up data mining algorithms for data of both high dimensionality and cardinality has been lately recognized as one of the most challenging problems in data mining research. The reason is that typical data mining tasks, such as clustering, cannot produce high quality results when applied on high-dimensional and/or large (in terms of cardinality) datasets. Data preprocessing and in particular dimensionality reduction constitute promising tools to deal with this problem. However, most of the existing dimensionality reduction algorithms share also the same disadvantages with data mining algorithms, when applied on large datasets of high dimensionality. In this article, we propose a fast and ef cient dimensionality reduction algorithm (FEDRA), which is particularly scalable and therefore suitable for challenging datasets. FEDRA follows the landmark-based paradigm for embedding data objects in a low-dimensional projection space. By means of a theoretical analysis, we prove that FEDRA is ef cient, while we demonstrate the achieved quality of results http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Knowledge Discovery from Data (TKDD) Association for Computing Machinery

Enhancing Clustering Quality through Landmark-Based Dimensionality Reduction

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References (50)

Publisher
Association for Computing Machinery
Copyright
Copyright © 2011 by ACM Inc.
ISSN
1556-4681
DOI
10.1145/1921632.1921637
Publisher site
See Article on Publisher Site

Abstract

TKD00016 ACM (Typeset by SPi, Manila, Philippines) 1 of 44 February 22, 2011 Enhancing Clustering Quality through Landmark-Based Dimensionality Reduction PANAGIS MAGDALINOS, Athens University of Economics and Business CHRISTOS DOULKERIDIS, Norwegian University of Science and Technology MICHALIS VAZIRGIANNIS, Athens University of Economics and Business Scaling up data mining algorithms for data of both high dimensionality and cardinality has been lately recognized as one of the most challenging problems in data mining research. The reason is that typical data mining tasks, such as clustering, cannot produce high quality results when applied on high-dimensional and/or large (in terms of cardinality) datasets. Data preprocessing and in particular dimensionality reduction constitute promising tools to deal with this problem. However, most of the existing dimensionality reduction algorithms share also the same disadvantages with data mining algorithms, when applied on large datasets of high dimensionality. In this article, we propose a fast and ef cient dimensionality reduction algorithm (FEDRA), which is particularly scalable and therefore suitable for challenging datasets. FEDRA follows the landmark-based paradigm for embedding data objects in a low-dimensional projection space. By means of a theoretical analysis, we prove that FEDRA is ef cient, while we demonstrate the achieved quality of results

Journal

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

Published: Feb 1, 2011

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