A Framework for Evaluation and Exploration of Clustering Algorithms in Subspaces of High Dimensional Databases

Research output: Contribution to book/anthology/report/proceedingArticle in proceedingsResearchpeer-review

Standard

A Framework for Evaluation and Exploration of Clustering Algorithms in Subspaces of High Dimensional Databases. / Müller, Emmanuel; Assent, Ira; Günnemann, Stephan; Gerwert, Patrick; Hannen, Matthias; Jansen, Timm; Seidl, Thomas.

Proceedings of the 14th GI Conference on Database Systems for Business, Technology, and the Web (BTW 2011). ed. / Theo Härder; Wolfgang Lehner; Bernhard Mitschang; Harald Schöning; Holger Schwarz. Gesellschaft für Informatik e.V., 2011. p. 347-366.

Research output: Contribution to book/anthology/report/proceedingArticle in proceedingsResearchpeer-review

Harvard

Müller, E, Assent, I, Günnemann, S, Gerwert, P, Hannen, M, Jansen, T & Seidl, T 2011, A Framework for Evaluation and Exploration of Clustering Algorithms in Subspaces of High Dimensional Databases. in T Härder, W Lehner, B Mitschang, H Schöning & H Schwarz (eds), Proceedings of the 14th GI Conference on Database Systems for Business, Technology, and the Web (BTW 2011). Gesellschaft für Informatik e.V., pp. 347-366, 14th BTW conference on Database Systems for Business, Technology and Web, Kaiserslautern, Germany, 02/03/2011.

APA

Müller, E., Assent, I., Günnemann, S., Gerwert, P., Hannen, M., Jansen, T., & Seidl, T. (2011). A Framework for Evaluation and Exploration of Clustering Algorithms in Subspaces of High Dimensional Databases. In T. Härder, W. Lehner, B. Mitschang, H. Schöning, & H. Schwarz (Eds.), Proceedings of the 14th GI Conference on Database Systems for Business, Technology, and the Web (BTW 2011) (pp. 347-366). Gesellschaft für Informatik e.V..

CBE

Müller E, Assent I, Günnemann S, Gerwert P, Hannen M, Jansen T, Seidl T. 2011. A Framework for Evaluation and Exploration of Clustering Algorithms in Subspaces of High Dimensional Databases. Härder T, Lehner W, Mitschang B, Schöning H, Schwarz H, editors. In Proceedings of the 14th GI Conference on Database Systems for Business, Technology, and the Web (BTW 2011). Gesellschaft für Informatik e.V. pp. 347-366.

MLA

Müller, Emmanuel et al. "A Framework for Evaluation and Exploration of Clustering Algorithms in Subspaces of High Dimensional Databases"., Härder, Theo, Lehner, Wolfgang Mitschang, Bernhard Schöning, Harald Schwarz, Holger (editors). Proceedings of the 14th GI Conference on Database Systems for Business, Technology, and the Web (BTW 2011). Gesellschaft für Informatik e.V. 2011, 347-366.

Vancouver

Müller E, Assent I, Günnemann S, Gerwert P, Hannen M, Jansen T et al. A Framework for Evaluation and Exploration of Clustering Algorithms in Subspaces of High Dimensional Databases. In Härder T, Lehner W, Mitschang B, Schöning H, Schwarz H, editors, Proceedings of the 14th GI Conference on Database Systems for Business, Technology, and the Web (BTW 2011). Gesellschaft für Informatik e.V. 2011. p. 347-366

Author

Müller, Emmanuel ; Assent, Ira ; Günnemann, Stephan ; Gerwert, Patrick ; Hannen, Matthias ; Jansen, Timm ; Seidl, Thomas. / A Framework for Evaluation and Exploration of Clustering Algorithms in Subspaces of High Dimensional Databases. Proceedings of the 14th GI Conference on Database Systems for Business, Technology, and the Web (BTW 2011). editor / Theo Härder ; Wolfgang Lehner ; Bernhard Mitschang ; Harald Schöning ; Holger Schwarz. Gesellschaft für Informatik e.V., 2011. pp. 347-366

Bibtex

@inproceedings{e552d19b750c4c9495547133fc032bd9,
title = "A Framework for Evaluation and Exploration of Clustering Algorithms in Subspaces of High Dimensional Databases",
abstract = "In high dimensional databases, traditional full space clustering methods are known to fail due to the curse of dimensionality. Thus, in recent years, subspace clustering and projected clustering approaches were proposed for clustering in high dimensional spaces. As the area is rather young, few comparative studies on the advantages and disadvantages of the different algorithms exist. Part of the underlying problem is the lack of available open source implementations that could be used by researchers to understand, compare, and extend subspace and projected clustering algorithms. In this work, we discuss the requirements for open source evaluation software and propose OpenSubspace that meets these requirements. OpenSubspace integrates state-of-the-art performance measures and visualization techniques to foster research in clustering in high dimensional databases.",
author = "Emmanuel M{\"u}ller and Ira Assent and Stephan G{\"u}nnemann and Patrick Gerwert and Matthias Hannen and Timm Jansen and Thomas Seidl",
year = "2011",
language = "English",
isbn = "978-3-88579-274-1",
pages = "347--366",
editor = "Theo H{\"a}rder and Wolfgang Lehner and Bernhard Mitschang and Harald Sch{\"o}ning and Holger Schwarz",
booktitle = "Proceedings of the 14th GI Conference on Database Systems for Business, Technology, and the Web (BTW 2011)",
publisher = "Gesellschaft f{\"u}r Informatik e.V.",

}

RIS

TY - GEN

T1 - A Framework for Evaluation and Exploration of Clustering Algorithms in Subspaces of High Dimensional Databases

AU - Müller, Emmanuel

AU - Assent, Ira

AU - Günnemann, Stephan

AU - Gerwert, Patrick

AU - Hannen, Matthias

AU - Jansen, Timm

AU - Seidl, Thomas

PY - 2011

Y1 - 2011

N2 - In high dimensional databases, traditional full space clustering methods are known to fail due to the curse of dimensionality. Thus, in recent years, subspace clustering and projected clustering approaches were proposed for clustering in high dimensional spaces. As the area is rather young, few comparative studies on the advantages and disadvantages of the different algorithms exist. Part of the underlying problem is the lack of available open source implementations that could be used by researchers to understand, compare, and extend subspace and projected clustering algorithms. In this work, we discuss the requirements for open source evaluation software and propose OpenSubspace that meets these requirements. OpenSubspace integrates state-of-the-art performance measures and visualization techniques to foster research in clustering in high dimensional databases.

AB - In high dimensional databases, traditional full space clustering methods are known to fail due to the curse of dimensionality. Thus, in recent years, subspace clustering and projected clustering approaches were proposed for clustering in high dimensional spaces. As the area is rather young, few comparative studies on the advantages and disadvantages of the different algorithms exist. Part of the underlying problem is the lack of available open source implementations that could be used by researchers to understand, compare, and extend subspace and projected clustering algorithms. In this work, we discuss the requirements for open source evaluation software and propose OpenSubspace that meets these requirements. OpenSubspace integrates state-of-the-art performance measures and visualization techniques to foster research in clustering in high dimensional databases.

M3 - Article in proceedings

SN - 978-3-88579-274-1

SP - 347

EP - 366

BT - Proceedings of the 14th GI Conference on Database Systems for Business, Technology, and the Web (BTW 2011)

A2 - Härder, Theo

A2 - Lehner, Wolfgang

A2 - Mitschang, Bernhard

A2 - Schöning, Harald

A2 - Schwarz, Holger

PB - Gesellschaft für Informatik e.V.

ER -