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

Ke Bing Zhang; Mehmet A. Orgun; Kang Zhang

doi:10.1007/978-3-540-74976-9_32

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/Conference Proceeding/Report › Conference Paper published in a book › peer-review

11 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
Subtitle of host publication	PKDD 2007 - 11th European Conference on Principles and Practice of Knowledge Discovery in Databases, Proceedings
Publisher	Springer Verlag
Pages	336-349
Number of pages	14
ISBN (Print)	9783540749752
DOIs	https://doi.org/10.1007/978-3-540-74976-9_32
Publication status	Published - 2007
Externally published	Yes
Event	11th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2007 - Warsaw, Poland Duration: 17 Sept 2007 → 21 Sept 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)	0302-9743
ISSN (Electronic)	1611-3349

Conference

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

Keywords

Cluster analysis
Predictive knowledge discovery
Visualization

Access to Document

10.1007/978-3-540-74976-9_32

Cite this

Zhang, K. B., Orgun, M. A., & Zhang, K. (2007). A prediction-based visual approach for cluster exploration and cluster validation by HOV. In Knowledge Discovery in Database: PKDD 2007 - 11th European Conference on Principles and Practice of Knowledge Discovery in Databases, Proceedings (pp. 336-349). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4702 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-540-74976-9_32

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. Springer Verlag, 2007. pp. 336-349 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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.",

keywords = "Cluster analysis, Predictive knowledge discovery, Visualization",

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

year = "2007",

doi = "10.1007/978-3-540-74976-9\_32",

language = "English",

isbn = "9783540749752",

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

publisher = "Springer Verlag",

pages = "336--349",

booktitle = "Knowledge Discovery in Database",

note = "11th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2007 ; Conference date: 17-09-2007 Through 21-09-2007",

}

Zhang, KB, Orgun, MA & Zhang, K 2007, A prediction-based visual approach for cluster exploration and cluster validation by HOV. in Knowledge Discovery in Database: PKDD 2007 - 11th European Conference on Principles and Practice of Knowledge Discovery in Databases, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4702 LNAI, Springer Verlag, pp. 336-349, 11th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2007, Warsaw, Poland, 17/09/07. https://doi.org/10.1007/978-3-540-74976-9_32

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. Springer Verlag, 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/Conference Proceeding/Report › Conference Paper published in a book › peer-review

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.

KW - Cluster analysis

KW - Predictive knowledge discovery

KW - Visualization

UR - https://www.scopus.com/pages/publications/38049113247

U2 - 10.1007/978-3-540-74976-9_32

DO - 10.1007/978-3-540-74976-9_32

M3 - Conference Paper published in a book

AN - SCOPUS:38049113247

SN - 9783540749752

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

PB - Springer Verlag

T2 - 11th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2007

Y2 - 17 September 2007 through 21 September 2007

ER -

Zhang KB, Orgun MA, Zhang K. A prediction-based visual approach for cluster exploration and cluster validation by HOV. In Knowledge Discovery in Database: PKDD 2007 - 11th European Conference on Principles and Practice of Knowledge Discovery in Databases, Proceedings. Springer Verlag. 2007. p. 336-349. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-74976-9_32

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

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