The discovery of hierarchical cluster structures assisted by a visualization technique

Ke Bing Zhang; Mehmet A. Orgun; Yanchang Zhao; Abhaya C. Nayak

doi:10.1007/978-3-642-17537-4_85

The discovery of hierarchical cluster structures assisted by a visualization technique

Ke Bing Zhang^*, Mehmet A. Orgun, Yanchang Zhao, Abhaya C. Nayak

^*Corresponding author for this work

Department of Computing

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

Abstract

Hierarchical clustering is very versatile in real world applications. However, due to the issue of higher computational complexity from which automated hierarchical clustering algorithms suffer, the user can hardly correct possible misclassifications from the tree-structured nature of clusters. Visualization is a powerful technique for data analysis, however, most of the existing cluster visualization techniques are mainly used for displaying clustering results. In order for the user to be directly involved in the process of discovering nested cluster structures, we introduce a visualization technique, called HOV³, to detect clusters and their internal cluster structure. As a result, our approach provides the user an effective method for the discovery of nested cluster structures by visualization.

Original language	English
Title of host publication	Neural Information Processing: Theory and Algorithms - 17th International Conference, ICONIP 2010, Proceedings
Editors	Kok Wai Wong, B. Mendis, U. Sumudu, Abdesselam Bouzerdoum
Place of Publication	Berlin
Publisher	Springer, Springer Nature
Pages	703-711
Number of pages	9
Volume	6443 LNCS
Edition	PART 1
ISBN (Print)	3642175368, 9783642175367
DOIs	https://doi.org/10.1007/978-3-642-17537-4_85
Publication status	Published - 2010
Event	17th International Conference on Neural Information Processing, ICONIP 2010 - Sydney, NSW, Australia Duration: 22 Nov 2010 → 25 Nov 2010

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 1
Volume	6443 LNCS
ISSN (Print)	03029743
ISSN (Electronic)	16113349

Other

Other	17th International Conference on Neural Information Processing, ICONIP 2010
Country	Australia
City	Sydney, NSW
Period	22/11/10 → 25/11/10

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Cite this

Zhang, K. B., Orgun, M. A., Zhao, Y., & Nayak, A. C. (2010). The discovery of hierarchical cluster structures assisted by a visualization technique. In K. W. Wong, B. Mendis, U. Sumudu, & A. Bouzerdoum (Eds.), Neural Information Processing: Theory and Algorithms - 17th International Conference, ICONIP 2010, Proceedings (PART 1 ed., Vol. 6443 LNCS, pp. 703-711). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6443 LNCS, No. PART 1). Berlin: Springer, Springer Nature. https://doi.org/10.1007/978-3-642-17537-4_85

Zhang, Ke Bing ; Orgun, Mehmet A. ; Zhao, Yanchang ; Nayak, Abhaya C. / The discovery of hierarchical cluster structures assisted by a visualization technique. Neural Information Processing: Theory and Algorithms - 17th International Conference, ICONIP 2010, Proceedings. editor / Kok Wai Wong ; B. Mendis ; U. Sumudu ; Abdesselam Bouzerdoum. Vol. 6443 LNCS PART 1. ed. Berlin : Springer, Springer Nature, 2010. pp. 703-711 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).

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title = "The discovery of hierarchical cluster structures assisted by a visualization technique",

abstract = "Hierarchical clustering is very versatile in real world applications. However, due to the issue of higher computational complexity from which automated hierarchical clustering algorithms suffer, the user can hardly correct possible misclassifications from the tree-structured nature of clusters. Visualization is a powerful technique for data analysis, however, most of the existing cluster visualization techniques are mainly used for displaying clustering results. In order for the user to be directly involved in the process of discovering nested cluster structures, we introduce a visualization technique, called HOV3, to detect clusters and their internal cluster structure. As a result, our approach provides the user an effective method for the discovery of nested cluster structures by visualization.",

author = "Zhang, {Ke Bing} and Orgun, {Mehmet A.} and Yanchang Zhao and Nayak, {Abhaya C.}",

year = "2010",

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language = "English",

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pages = "703--711",

editor = "Wong, {Kok Wai} and B. Mendis and U. Sumudu and Abdesselam Bouzerdoum",

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Zhang, KB, Orgun, MA, Zhao, Y & Nayak, AC 2010, The discovery of hierarchical cluster structures assisted by a visualization technique. in KW Wong, B Mendis, U Sumudu & A Bouzerdoum (eds), Neural Information Processing: Theory and Algorithms - 17th International Conference, ICONIP 2010, Proceedings. PART 1 edn, vol. 6443 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 6443 LNCS, Springer, Springer Nature, Berlin, pp. 703-711, 17th International Conference on Neural Information Processing, ICONIP 2010, Sydney, NSW, Australia, 22/11/10. https://doi.org/10.1007/978-3-642-17537-4_85

The discovery of hierarchical cluster structures assisted by a visualization technique. / Zhang, Ke Bing; Orgun, Mehmet A.; Zhao, Yanchang; Nayak, Abhaya C.

Neural Information Processing: Theory and Algorithms - 17th International Conference, ICONIP 2010, Proceedings. ed. / Kok Wai Wong; B. Mendis; U. Sumudu; Abdesselam Bouzerdoum. Vol. 6443 LNCS PART 1. ed. Berlin : Springer, Springer Nature, 2010. p. 703-711 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6443 LNCS, No. PART 1).

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

TY - GEN

T1 - The discovery of hierarchical cluster structures assisted by a visualization technique

AU - Zhang, Ke Bing

AU - Orgun, Mehmet A.

AU - Zhao, Yanchang

AU - Nayak, Abhaya C.

PY - 2010

Y1 - 2010

N2 - Hierarchical clustering is very versatile in real world applications. However, due to the issue of higher computational complexity from which automated hierarchical clustering algorithms suffer, the user can hardly correct possible misclassifications from the tree-structured nature of clusters. Visualization is a powerful technique for data analysis, however, most of the existing cluster visualization techniques are mainly used for displaying clustering results. In order for the user to be directly involved in the process of discovering nested cluster structures, we introduce a visualization technique, called HOV3, to detect clusters and their internal cluster structure. As a result, our approach provides the user an effective method for the discovery of nested cluster structures by visualization.

AB - Hierarchical clustering is very versatile in real world applications. However, due to the issue of higher computational complexity from which automated hierarchical clustering algorithms suffer, the user can hardly correct possible misclassifications from the tree-structured nature of clusters. Visualization is a powerful technique for data analysis, however, most of the existing cluster visualization techniques are mainly used for displaying clustering results. In order for the user to be directly involved in the process of discovering nested cluster structures, we introduce a visualization technique, called HOV3, to detect clusters and their internal cluster structure. As a result, our approach provides the user an effective method for the discovery of nested cluster structures by visualization.

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BT - Neural Information Processing: Theory and Algorithms - 17th International Conference, ICONIP 2010, Proceedings

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Zhang KB, Orgun MA, Zhao Y, Nayak AC. The discovery of hierarchical cluster structures assisted by a visualization technique. In Wong KW, Mendis B, Sumudu U, Bouzerdoum A, editors, Neural Information Processing: Theory and Algorithms - 17th International Conference, ICONIP 2010, Proceedings. PART 1 ed. Vol. 6443 LNCS. Berlin: Springer, Springer Nature. 2010. p. 703-711. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). https://doi.org/10.1007/978-3-642-17537-4_85

Access to Document

10.1007/978-3-642-17537-4_85

Link to publication in Scopus