A prediction-based visual approach for cluster exploration and cluster validation by HOV

Ke Bing Zhang; Mehmet A. Orgun; Kang Zhang

A prediction-based visual approach for cluster exploration and cluster validation by HOV

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

^*Corresponding author for this work

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

9 Citations (Scopus)

Abstract

Predictive knowledge discovery is an important knowledge acquisition method. It is also used in the clustering process of data mining. Visualization is very helpful for high dimensional data analysis, but not precise and this limits its usability in quantitative cluster analysis. In this paper, we adopt a visual technique called HOV³ to explore and verify clustering results with quantified measurements. With the quantified contrast between grouped data distributions produced by HOV³, users can detect clusters and verify their validity efficiently.

Original language	English
Title of host publication	Knowledge Discovery in Database: PKDD 2007 - 11th European Conference on Principles and Practice of Knowledge Discovery in Databases, Proceedings
Editors	Joost N. Kok, Jacek Koronacki, Ramon Lopez de Mantaras, Stan Matwin, Dunja Mladenic, Andrzej Skowron
Place of Publication	Berlin; Heidelberg
Publisher	Springer, Springer Nature
Pages	336-349
Number of pages	14
Volume	4702 LNAI
ISBN (Print)	9783540749752
Publication status	Published - 2007
Event	11th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2007 - Warsaw, Poland Duration: 17 Sep 2007 → 21 Sep 2007

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	4702 LNAI
ISSN (Print)	03029743
ISSN (Electronic)	16113349

Other

Other	11th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2007
Country	Poland
City	Warsaw
Period	17/09/07 → 21/09/07

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Zhang, K. B., Orgun, M. A., & Zhang, K. (2007). A prediction-based visual approach for cluster exploration and cluster validation by HOV. In J. N. Kok, J. Koronacki, R. L. de Mantaras, S. Matwin, D. Mladenic, & A. Skowron (Eds.), Knowledge Discovery in Database: PKDD 2007 - 11th European Conference on Principles and Practice of Knowledge Discovery in Databases, Proceedings (Vol. 4702 LNAI, pp. 336-349). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4702 LNAI). Berlin; Heidelberg: Springer, Springer Nature.

Zhang, Ke Bing ; Orgun, Mehmet A. ; Zhang, Kang. / A prediction-based visual approach for cluster exploration and cluster validation by HOV. Knowledge Discovery in Database: PKDD 2007 - 11th European Conference on Principles and Practice of Knowledge Discovery in Databases, Proceedings. editor / Joost N. Kok ; Jacek Koronacki ; Ramon Lopez de Mantaras ; Stan Matwin ; Dunja Mladenic ; Andrzej Skowron. Vol. 4702 LNAI Berlin; Heidelberg : Springer, Springer Nature, 2007. pp. 336-349 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{51c4ad442c9c403abfe4d4fb820fc179,

title = "A prediction-based visual approach for cluster exploration and cluster validation by HOV",

abstract = "Predictive knowledge discovery is an important knowledge acquisition method. It is also used in the clustering process of data mining. Visualization is very helpful for high dimensional data analysis, but not precise and this limits its usability in quantitative cluster analysis. In this paper, we adopt a visual technique called HOV3 to explore and verify clustering results with quantified measurements. With the quantified contrast between grouped data distributions produced by HOV3, users can detect clusters and verify their validity efficiently.",

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

year = "2007",

language = "English",

isbn = "9783540749752",

volume = "4702 LNAI",

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

publisher = "Springer, Springer Nature",

pages = "336--349",

editor = "Kok, {Joost N.} and Jacek Koronacki and {de Mantaras}, {Ramon Lopez} and Stan Matwin and Dunja Mladenic and Andrzej Skowron",

booktitle = "Knowledge Discovery in Database: PKDD 2007 - 11th European Conference on Principles and Practice of Knowledge Discovery in Databases, Proceedings",

address = "United States",

}

Zhang, KB, Orgun, MA & Zhang, K 2007, A prediction-based visual approach for cluster exploration and cluster validation by HOV. in JN Kok, J Koronacki, RL de Mantaras, S Matwin, D Mladenic & A Skowron (eds), Knowledge Discovery in Database: PKDD 2007 - 11th European Conference on Principles and Practice of Knowledge Discovery in Databases, Proceedings. vol. 4702 LNAI, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4702 LNAI, Springer, Springer Nature, Berlin; Heidelberg, pp. 336-349, 11th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2007, Warsaw, Poland, 17/09/07.

A prediction-based visual approach for cluster exploration and cluster validation by HOV. / Zhang, Ke Bing; Orgun, Mehmet A.; Zhang, Kang.

Knowledge Discovery in Database: PKDD 2007 - 11th European Conference on Principles and Practice of Knowledge Discovery in Databases, Proceedings. ed. / Joost N. Kok; Jacek Koronacki; Ramon Lopez de Mantaras; Stan Matwin; Dunja Mladenic; Andrzej Skowron. Vol. 4702 LNAI Berlin; Heidelberg : Springer, Springer Nature, 2007. p. 336-349 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4702 LNAI).

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

TY - GEN

T1 - A prediction-based visual approach for cluster exploration and cluster validation by HOV

AU - Zhang, Ke Bing

AU - Orgun, Mehmet A.

AU - Zhang, Kang

PY - 2007

Y1 - 2007

N2 - Predictive knowledge discovery is an important knowledge acquisition method. It is also used in the clustering process of data mining. Visualization is very helpful for high dimensional data analysis, but not precise and this limits its usability in quantitative cluster analysis. In this paper, we adopt a visual technique called HOV3 to explore and verify clustering results with quantified measurements. With the quantified contrast between grouped data distributions produced by HOV3, users can detect clusters and verify their validity efficiently.

AB - Predictive knowledge discovery is an important knowledge acquisition method. It is also used in the clustering process of data mining. Visualization is very helpful for high dimensional data analysis, but not precise and this limits its usability in quantitative cluster analysis. In this paper, we adopt a visual technique called HOV3 to explore and verify clustering results with quantified measurements. With the quantified contrast between grouped data distributions produced by HOV3, users can detect clusters and verify their validity efficiently.

UR - http://www.scopus.com/inward/record.url?scp=38049113247&partnerID=8YFLogxK

M3 - Conference proceeding contribution

SN - 9783540749752

VL - 4702 LNAI

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 336

EP - 349

BT - Knowledge Discovery in Database: PKDD 2007 - 11th European Conference on Principles and Practice of Knowledge Discovery in Databases, Proceedings

A2 - Kok, Joost N.

A2 - Koronacki, Jacek

A2 - de Mantaras, Ramon Lopez

A2 - Matwin, Stan

A2 - Mladenic, Dunja

A2 - Skowron, Andrzej

PB - Springer, Springer Nature

CY - Berlin; Heidelberg

ER -

Zhang KB, Orgun MA, Zhang K. A prediction-based visual approach for cluster exploration and cluster validation by HOV. In Kok JN, Koronacki J, de Mantaras RL, Matwin S, Mladenic D, Skowron A, editors, Knowledge Discovery in Database: PKDD 2007 - 11th European Conference on Principles and Practice of Knowledge Discovery in Databases, Proceedings. Vol. 4702 LNAI. Berlin; Heidelberg: Springer, Springer Nature. 2007. p. 336-349. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).