Unsupervised learning: Self-aggregation in scaled principal component space

Chris Ding; Xiaofeng He; Hongyuan Zha; Horst Simon

doi:10.1007/3-540-45681-3_10

Unsupervised learning: Self-aggregation in scaled principal component space

Chris Ding^*
, Xiaofeng He
, Hongyuan Zha
, Horst Simon

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

19 Scopus citations

Abstract

We demonstrate that data clustering amounts to a dynamic process of self-aggregation in which data objects move towards each other to form clusters, revealing the inherent pattern of similarity. Selfaggregation is governed by connectivity and occurs in a space obtained by a nonlinear scaling of principal component analysis (PCA). The method combines dimensionality reduction with clustering into a single framework. It can apply to both square similarity matrices and rectangular association matrices.

Original language	English
Title of host publication	Principles of Data Mining and Knowledge Discovery - 6th European Conference, PKDD 2002, Proceedings
Editors	Tapio Elomaa, Heikki Mannila, Hannu Toivonen
Publisher	Springer Verlag
Pages	112-124
Number of pages	13
ISBN (Print)	3540440372, 9783540440376
DOIs	https://doi.org/10.1007/3-540-45681-3_10
State	Published - 2002
Externally published	Yes
Event	6th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2002 - Helsinki, Finland Duration: 19 Aug 2002 → 23 Aug 2002

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	2431 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	6th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2002
Country/Territory	Finland
City	Helsinki
Period	19/08/02 → 23/08/02

Access to Document

10.1007/3-540-45681-3_10

Cite this

Ding, C., He, X., Zha, H., & Simon, H. (2002). Unsupervised learning: Self-aggregation in scaled principal component space. In T. Elomaa, H. Mannila, & H. Toivonen (Eds.), Principles of Data Mining and Knowledge Discovery - 6th European Conference, PKDD 2002, Proceedings (pp. 112-124). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2431 LNAI). Springer Verlag. https://doi.org/10.1007/3-540-45681-3_10

Ding, Chris ; He, Xiaofeng ; Zha, Hongyuan et al. / Unsupervised learning : Self-aggregation in scaled principal component space. Principles of Data Mining and Knowledge Discovery - 6th European Conference, PKDD 2002, Proceedings. editor / Tapio Elomaa ; Heikki Mannila ; Hannu Toivonen. Springer Verlag, 2002. pp. 112-124 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Unsupervised learning: Self-aggregation in scaled principal component space",

abstract = "We demonstrate that data clustering amounts to a dynamic process of self-aggregation in which data objects move towards each other to form clusters, revealing the inherent pattern of similarity. Selfaggregation is governed by connectivity and occurs in a space obtained by a nonlinear scaling of principal component analysis (PCA). The method combines dimensionality reduction with clustering into a single framework. It can apply to both square similarity matrices and rectangular association matrices.",

author = "Chris Ding and Xiaofeng He and Hongyuan Zha and Horst Simon",

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doi = "10.1007/3-540-45681-3\_10",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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booktitle = "Principles of Data Mining and Knowledge Discovery - 6th European Conference, PKDD 2002, Proceedings",

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Ding, C, He, X, Zha, H & Simon, H 2002, Unsupervised learning: Self-aggregation in scaled principal component space. in T Elomaa, H Mannila & H Toivonen (eds), Principles of Data Mining and Knowledge Discovery - 6th European Conference, PKDD 2002, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2431 LNAI, Springer Verlag, pp. 112-124, 6th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2002, Helsinki, Finland, 19/08/02. https://doi.org/10.1007/3-540-45681-3_10

Unsupervised learning: Self-aggregation in scaled principal component space. / Ding, Chris; He, Xiaofeng; Zha, Hongyuan et al.
Principles of Data Mining and Knowledge Discovery - 6th European Conference, PKDD 2002, Proceedings. ed. / Tapio Elomaa; Heikki Mannila; Hannu Toivonen. Springer Verlag, 2002. p. 112-124 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2431 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - We demonstrate that data clustering amounts to a dynamic process of self-aggregation in which data objects move towards each other to form clusters, revealing the inherent pattern of similarity. Selfaggregation is governed by connectivity and occurs in a space obtained by a nonlinear scaling of principal component analysis (PCA). The method combines dimensionality reduction with clustering into a single framework. It can apply to both square similarity matrices and rectangular association matrices.

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SN - 9783540440376

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Ding C, He X, Zha H, Simon H. Unsupervised learning: Self-aggregation in scaled principal component space. In Elomaa T, Mannila H, Toivonen H, editors, Principles of Data Mining and Knowledge Discovery - 6th European Conference, PKDD 2002, Proceedings. Springer Verlag. 2002. p. 112-124. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/3-540-45681-3_10

Unsupervised learning: Self-aggregation in scaled principal component space

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