A visual approach for external cluster validation

Ke Bing Zhang; Mehmet A. Orgun; Kang Zhang

doi:10.1109/CIDM.2007.368927

A visual approach for external cluster validation

Ke Bing Zhang^*, Mehmet A. Orgun, Kang Zhang

^*Corresponding author for this work

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

7 Citations (Scopus)

44 Downloads (Pure)

Abstract

Visualization can be very powerful in revealing cluster structures. However, directly using visualization techniques to verify the validity of clustering results is still a challenge. This is due to the fact that visual representation lacks precision in contrasting clustering results. To remedy this problem, in this paper we propose a novel approach, which employs a visualization technique called HOV (Hypothesis Oriented Verification and Validation by Visualization) which offers a tunable measure mechanism to project clustered subsets and non-clustered subsets from a multidimensional space to a 2D plane. By comparing the data distributions of the subsets, users not only have an intuitive visual evaluation but also have a precise evaluation on the consistency of cluster structure by calculating geometrical information of their data distributions.

Original language	English
Title of host publication	Proceedings of the 2007 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2007
Editors	Wlodek Duch, Joydeep Ghosh
Place of Publication	Piscataway, NJ
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	576-582
Number of pages	7
ISBN (Print)	1424407052, 9781424407057
DOIs	https://doi.org/10.1109/CIDM.2007.368927
Publication status	Published - 2007
Event	1st IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2007 - Honolulu, HI, United States Duration: 1 Apr 2007 → 5 Apr 2007

Other

Other	1st IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2007
Country/Territory	United States
City	Honolulu, HI
Period	1/04/07 → 5/04/07

Bibliographical note

Copyright 2007 IEEE. Reprinted from Proceedings of the 2007 IEEE symposium on computational intelligence and data mining (CIDM 2007). This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Macquarie Universityâ€™s products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.

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title = "A visual approach for external cluster validation",

abstract = "Visualization can be very powerful in revealing cluster structures. However, directly using visualization techniques to verify the validity of clustering results is still a challenge. This is due to the fact that visual representation lacks precision in contrasting clustering results. To remedy this problem, in this paper we propose a novel approach, which employs a visualization technique called HOV (Hypothesis Oriented Verification and Validation by Visualization) which offers a tunable measure mechanism to project clustered subsets and non-clustered subsets from a multidimensional space to a 2D plane. By comparing the data distributions of the subsets, users not only have an intuitive visual evaluation but also have a precise evaluation on the consistency of cluster structure by calculating geometrical information of their data distributions.",

author = "Zhang, \{Ke Bing\} and Orgun, \{Mehmet A.\} and Kang Zhang",

note = "Copyright 2007 IEEE. Reprinted from Proceedings of the 2007 IEEE symposium on computational intelligence and data mining (CIDM 2007). This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Macquarie University{\^a}€{\texttrademark}s products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.; 1st IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2007 ; Conference date: 01-04-2007 Through 05-04-2007",

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Zhang, KB, Orgun, MA & Zhang, K 2007, A visual approach for external cluster validation. in W Duch & J Ghosh (eds), Proceedings of the 2007 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2007., 4221351, Institute of Electrical and Electronics Engineers (IEEE), Piscataway, NJ, pp. 576-582, 1st IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2007, Honolulu, HI, United States, 1/04/07. https://doi.org/10.1109/CIDM.2007.368927

A visual approach for external cluster validation. / Zhang, Ke Bing; Orgun, Mehmet A.; Zhang, Kang.
Proceedings of the 2007 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2007. ed. / Wlodek Duch; Joydeep Ghosh. Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2007. p. 576-582 4221351.

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

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N1 - Copyright 2007 IEEE. Reprinted from Proceedings of the 2007 IEEE symposium on computational intelligence and data mining (CIDM 2007). This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Macquarie Universityâ€™s products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.

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ER -

A visual approach for external cluster validation

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