M-FastMap: A Modified FastMap Algorithm for Visual Cluster Validation in Data Mining

Michael Ng; Joshua Huang

doi:10.1007/3-540-47887-6_22

M-FastMap: A Modified FastMap Algorithm for Visual Cluster Validation in Data Mining

Michael Ng, Joshua Huang

Department of Mathematics

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

2 Citations (Scopus)

Abstract

This paper presents M-FastMap, a modified FastMap algorithm for visual cluster validation in data mining. In the visual cluster validation with FastMap, clusters are first generated with a clustering algorithm from a database. Then, the FastMap algorithm is used to project the clusters onto a 2-dimensional (2D) or 3-dimensional (3D) space and the clusters are visualized with different colors and/or symbols on a 2D (or 3D) display. From the display a human can visually examine the separation of clusters. This method follows the principle that if a cluster is separate from others in the projected 2D (or 3D) space, it is also separate from others in the original high dimensional space (the opposite is not true). The modified FastMap algorithm improves the quality of visual cluster validation by optimizing the separation of clusters on the 2D or (3D) space in the selection of pivot objects (or projection axis). The comparison study has shown that the modified FastMap algorithm can produce better visualization results than the original FastMap algorithm.

Original language	English
Title of host publication	Advances in Knowledge Discovery and Data Mining
Subtitle of host publication	6th Pacific-Asia Conference, PAKDD 2002, Taipei, Taiwan, May 6-8, 2002. Proceedings
Editors	Ming-Syan Chen, Philip S. Yu, Bing Liu
Publisher	Springer Berlin Heidelberg
Pages	224-236
Number of pages	13
Edition	1st
ISBN (Electronic)	9783540478874
ISBN (Print)	9783540437048
DOIs	https://doi.org/10.1007/3-540-47887-6_22
Publication status	Published - 29 Apr 2002
Event	6th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2002 - Taipei, Taiwan, Province of China Duration: 6 May 2002 → 8 May 2002 https://link.springer.com/book/10.1007/3-540-47887-6

Publication series

Name	Lecture Notes in Computer Science
Volume	2336
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Name	Lecture Notes in Artificial Intelligence
ISSN (Print)	2945-9133
ISSN (Electronic)	2945-9141

Name	Pacific-Asia Conference on Knowledge Discovery and Data Mining

Conference

Conference	6th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2002
Country/Territory	Taiwan, Province of China
City	Taipei
Period	6/05/02 → 8/05/02
Internet address	https://link.springer.com/book/10.1007/3-540-47887-6

Scopus Subject Areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/3-540-47887-6_22

Cite this

Ng, M., & Huang, J. (2002). M-FastMap: A Modified FastMap Algorithm for Visual Cluster Validation in Data Mining. In M.-S. Chen, P. S. Yu, & B. Liu (Eds.), Advances in Knowledge Discovery and Data Mining: 6th Pacific-Asia Conference, PAKDD 2002, Taipei, Taiwan, May 6-8, 2002. Proceedings (1st ed., pp. 224-236). (Lecture Notes in Computer Science; Vol. 2336), (Lecture Notes in Artificial Intelligence), (Pacific-Asia Conference on Knowledge Discovery and Data Mining). Springer Berlin Heidelberg. https://doi.org/10.1007/3-540-47887-6_22

Ng, Michael ; Huang, Joshua. / M-FastMap: A Modified FastMap Algorithm for Visual Cluster Validation in Data Mining. Advances in Knowledge Discovery and Data Mining: 6th Pacific-Asia Conference, PAKDD 2002, Taipei, Taiwan, May 6-8, 2002. Proceedings. editor / Ming-Syan Chen ; Philip S. Yu ; Bing Liu. 1st. ed. Springer Berlin Heidelberg, 2002. pp. 224-236 (Lecture Notes in Computer Science). (Lecture Notes in Artificial Intelligence). (Pacific-Asia Conference on Knowledge Discovery and Data Mining).

@inproceedings{f9839fd50c414ea88711440a8440ee00,

title = "M-FastMap: A Modified FastMap Algorithm for Visual Cluster Validation in Data Mining",

abstract = "This paper presents M-FastMap, a modified FastMap algorithm for visual cluster validation in data mining. In the visual cluster validation with FastMap, clusters are first generated with a clustering algorithm from a database. Then, the FastMap algorithm is used to project the clusters onto a 2-dimensional (2D) or 3-dimensional (3D) space and the clusters are visualized with different colors and/or symbols on a 2D (or 3D) display. From the display a human can visually examine the separation of clusters. This method follows the principle that if a cluster is separate from others in the projected 2D (or 3D) space, it is also separate from others in the original high dimensional space (the opposite is not true). The modified FastMap algorithm improves the quality of visual cluster validation by optimizing the separation of clusters on the 2D or (3D) space in the selection of pivot objects (or projection axis). The comparison study has shown that the modified FastMap algorithm can produce better visualization results than the original FastMap algorithm.",

author = "Michael Ng and Joshua Huang",

note = "supported in part by RGC Grant No. 7132/00P and HKU CRCG Grant Nos 10203501, 10203907 and 10203408. Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2002.; 6th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2002 ; Conference date: 06-05-2002 Through 08-05-2002",

year = "2002",

month = apr,

day = "29",

doi = "10.1007/3-540-47887-6_22",

language = "English",

isbn = "9783540437048",

series = "Lecture Notes in Computer Science",

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pages = "224--236",

editor = "Ming-Syan Chen and Yu, {Philip S.} and Bing Liu",

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Ng, M & Huang, J 2002, M-FastMap: A Modified FastMap Algorithm for Visual Cluster Validation in Data Mining. in M-S Chen, PS Yu & B Liu (eds), Advances in Knowledge Discovery and Data Mining: 6th Pacific-Asia Conference, PAKDD 2002, Taipei, Taiwan, May 6-8, 2002. Proceedings. 1st edn, Lecture Notes in Computer Science, vol. 2336, Lecture Notes in Artificial Intelligence, Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer Berlin Heidelberg, pp. 224-236, 6th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2002, Taipei, Taiwan, Province of China, 6/05/02. https://doi.org/10.1007/3-540-47887-6_22

M-FastMap: A Modified FastMap Algorithm for Visual Cluster Validation in Data Mining. / Ng, Michael; Huang, Joshua.
Advances in Knowledge Discovery and Data Mining: 6th Pacific-Asia Conference, PAKDD 2002, Taipei, Taiwan, May 6-8, 2002. Proceedings. ed. / Ming-Syan Chen; Philip S. Yu; Bing Liu. 1st. ed. Springer Berlin Heidelberg, 2002. p. 224-236 (Lecture Notes in Computer Science; Vol. 2336), (Lecture Notes in Artificial Intelligence), (Pacific-Asia Conference on Knowledge Discovery and Data Mining).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

TY - GEN

T1 - M-FastMap: A Modified FastMap Algorithm for Visual Cluster Validation in Data Mining

AU - Ng, Michael

AU - Huang, Joshua

PY - 2002/4/29

Y1 - 2002/4/29

N2 - This paper presents M-FastMap, a modified FastMap algorithm for visual cluster validation in data mining. In the visual cluster validation with FastMap, clusters are first generated with a clustering algorithm from a database. Then, the FastMap algorithm is used to project the clusters onto a 2-dimensional (2D) or 3-dimensional (3D) space and the clusters are visualized with different colors and/or symbols on a 2D (or 3D) display. From the display a human can visually examine the separation of clusters. This method follows the principle that if a cluster is separate from others in the projected 2D (or 3D) space, it is also separate from others in the original high dimensional space (the opposite is not true). The modified FastMap algorithm improves the quality of visual cluster validation by optimizing the separation of clusters on the 2D or (3D) space in the selection of pivot objects (or projection axis). The comparison study has shown that the modified FastMap algorithm can produce better visualization results than the original FastMap algorithm.

AB - This paper presents M-FastMap, a modified FastMap algorithm for visual cluster validation in data mining. In the visual cluster validation with FastMap, clusters are first generated with a clustering algorithm from a database. Then, the FastMap algorithm is used to project the clusters onto a 2-dimensional (2D) or 3-dimensional (3D) space and the clusters are visualized with different colors and/or symbols on a 2D (or 3D) display. From the display a human can visually examine the separation of clusters. This method follows the principle that if a cluster is separate from others in the projected 2D (or 3D) space, it is also separate from others in the original high dimensional space (the opposite is not true). The modified FastMap algorithm improves the quality of visual cluster validation by optimizing the separation of clusters on the 2D or (3D) space in the selection of pivot objects (or projection axis). The comparison study has shown that the modified FastMap algorithm can produce better visualization results than the original FastMap algorithm.

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T3 - Lecture Notes in Computer Science

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BT - Advances in Knowledge Discovery and Data Mining

A2 - Chen, Ming-Syan

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ER -

Ng M, Huang J. M-FastMap: A Modified FastMap Algorithm for Visual Cluster Validation in Data Mining. In Chen MS, Yu PS, Liu B, editors, Advances in Knowledge Discovery and Data Mining: 6th Pacific-Asia Conference, PAKDD 2002, Taipei, Taiwan, May 6-8, 2002. Proceedings. 1st ed. Springer Berlin Heidelberg. 2002. p. 224-236. (Lecture Notes in Computer Science). (Lecture Notes in Artificial Intelligence). (Pacific-Asia Conference on Knowledge Discovery and Data Mining). doi: 10.1007/3-540-47887-6_22

M-FastMap: A Modified FastMap Algorithm for Visual Cluster Validation in Data Mining

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