Enhanced visual separation of clusters by M-mapping to facilitate cluster analysis

Ke Bing Zhang; Mehmet A. Orgun; Kang Zhang

doi:10.1007/978-3-540-76414-4_29

Enhanced visual separation of clusters by M-mapping to facilitate cluster analysis

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

2 Citations (Scopus)

Abstract

The goal of clustering in data mining is to distinguish objects into partitions/clusters based on given criteria. Visualization methods and techniques may provide users an intuitively appealing interpretation of cluster structures. Having good visually separated groups of the studied data is beneficial for detecting cluster information as well as refining the membership formation of clusters. In this paper, we propose a novel visual approach called M-mapping, based on the projection technique of HOV³ to achieve the separation of cluster structures. With M-mapping, users can explore visual cluster clues intuitively and validate clusters effectively by matching the geometrical distributions of clustered and non-clustered subsets produced in HOV³.

Original language	English
Title of host publication	Advances in Visual Information Systems - 9th International Conference, VISUAL 2007, Revised Selected Papers
Publisher	Springer Verlag
Pages	285-297
Number of pages	13
ISBN (Print)	9783540764137
DOIs	https://doi.org/10.1007/978-3-540-76414-4_29
Publication status	Published - 2007
Externally published	Yes
Event	9th International Conference on Visual Information Systems, VISUAL 2007 - Shanghai, China Duration: 28 Jun 2007 → 29 Jun 2007

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	4781 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	9th International Conference on Visual Information Systems, VISUAL 2007
Country/Territory	China
City	Shanghai
Period	28/06/07 → 29/06/07

Keywords

Cluster analysis
Visual separability
Visualization

Access to Document

10.1007/978-3-540-76414-4_29

Cite this

Zhang, K. B., Orgun, M. A., & Zhang, K. (2007). Enhanced visual separation of clusters by M-mapping to facilitate cluster analysis. In Advances in Visual Information Systems - 9th International Conference, VISUAL 2007, Revised Selected Papers (pp. 285-297). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4781 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-540-76414-4_29

Zhang, Ke Bing ; Orgun, Mehmet A. ; Zhang, Kang. / Enhanced visual separation of clusters by M-mapping to facilitate cluster analysis. Advances in Visual Information Systems - 9th International Conference, VISUAL 2007, Revised Selected Papers. Springer Verlag, 2007. pp. 285-297 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Enhanced visual separation of clusters by M-mapping to facilitate cluster analysis",

abstract = "The goal of clustering in data mining is to distinguish objects into partitions/clusters based on given criteria. Visualization methods and techniques may provide users an intuitively appealing interpretation of cluster structures. Having good visually separated groups of the studied data is beneficial for detecting cluster information as well as refining the membership formation of clusters. In this paper, we propose a novel visual approach called M-mapping, based on the projection technique of HOV3 to achieve the separation of cluster structures. With M-mapping, users can explore visual cluster clues intuitively and validate clusters effectively by matching the geometrical distributions of clustered and non-clustered subsets produced in HOV3.",

keywords = "Cluster analysis, Visual separability, Visualization",

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

year = "2007",

doi = "10.1007/978-3-540-76414-4\_29",

language = "English",

isbn = "9783540764137",

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

publisher = "Springer Verlag",

pages = "285--297",

booktitle = "Advances in Visual Information Systems - 9th International Conference, VISUAL 2007, Revised Selected Papers",

note = "9th International Conference on Visual Information Systems, VISUAL 2007 ; Conference date: 28-06-2007 Through 29-06-2007",

}

Zhang, KB, Orgun, MA & Zhang, K 2007, Enhanced visual separation of clusters by M-mapping to facilitate cluster analysis. in Advances in Visual Information Systems - 9th International Conference, VISUAL 2007, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4781 LNCS, Springer Verlag, pp. 285-297, 9th International Conference on Visual Information Systems, VISUAL 2007, Shanghai, China, 28/06/07. https://doi.org/10.1007/978-3-540-76414-4_29

Enhanced visual separation of clusters by M-mapping to facilitate cluster analysis. / Zhang, Ke Bing; Orgun, Mehmet A.; Zhang, Kang.
Advances in Visual Information Systems - 9th International Conference, VISUAL 2007, Revised Selected Papers. Springer Verlag, 2007. p. 285-297 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4781 LNCS).

Research output: Chapter in Book/Conference Proceeding/Report › Conference Paper published in a book › peer-review

TY - GEN

T1 - Enhanced visual separation of clusters by M-mapping to facilitate cluster analysis

AU - Zhang, Ke Bing

AU - Orgun, Mehmet A.

AU - Zhang, Kang

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N2 - The goal of clustering in data mining is to distinguish objects into partitions/clusters based on given criteria. Visualization methods and techniques may provide users an intuitively appealing interpretation of cluster structures. Having good visually separated groups of the studied data is beneficial for detecting cluster information as well as refining the membership formation of clusters. In this paper, we propose a novel visual approach called M-mapping, based on the projection technique of HOV3 to achieve the separation of cluster structures. With M-mapping, users can explore visual cluster clues intuitively and validate clusters effectively by matching the geometrical distributions of clustered and non-clustered subsets produced in HOV3.

AB - The goal of clustering in data mining is to distinguish objects into partitions/clusters based on given criteria. Visualization methods and techniques may provide users an intuitively appealing interpretation of cluster structures. Having good visually separated groups of the studied data is beneficial for detecting cluster information as well as refining the membership formation of clusters. In this paper, we propose a novel visual approach called M-mapping, based on the projection technique of HOV3 to achieve the separation of cluster structures. With M-mapping, users can explore visual cluster clues intuitively and validate clusters effectively by matching the geometrical distributions of clustered and non-clustered subsets produced in HOV3.

KW - Cluster analysis

KW - Visual separability

KW - Visualization

UR - https://www.scopus.com/pages/publications/38349043758

U2 - 10.1007/978-3-540-76414-4_29

DO - 10.1007/978-3-540-76414-4_29

M3 - Conference Paper published in a book

AN - SCOPUS:38349043758

SN - 9783540764137

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

SP - 285

EP - 297

BT - Advances in Visual Information Systems - 9th International Conference, VISUAL 2007, Revised Selected Papers

PB - Springer Verlag

T2 - 9th International Conference on Visual Information Systems, VISUAL 2007

Y2 - 28 June 2007 through 29 June 2007

ER -

Zhang KB, Orgun MA, Zhang K. Enhanced visual separation of clusters by M-mapping to facilitate cluster analysis. In Advances in Visual Information Systems - 9th International Conference, VISUAL 2007, Revised Selected Papers. Springer Verlag. 2007. p. 285-297. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-76414-4_29

Enhanced visual separation of clusters by M-mapping to facilitate cluster analysis

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Keywords

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