Leveraging a web-aware self-organization map tool for clustering and visualization

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The self-organization map (SOM) neural network has been recognized as a successful paradigm for clustering and visualization in a large variety of real-world applications. There exist a number of useful stand-alone SOM tools, however, they cannot be adapted to the new generation web environment. In addition, different user interfaces required for operation and the heterogeneity of platforms where the tools run on prevent them from appeal. In this paper, we propose a web-aware SOM tool which integrates the computationally powerful SOM PAK and the vivid Nenet tools to augment the advantages of each. The proposed SOM tool is capable of delimiting the desired clusters by adopting two level network topology and silhouette coefficients.

Original languageEnglish
Title of host publicationWeb Intelligence
Subtitle of host publicationResearch and Development - 1st Asia-Pacific Conference, WI 2001, Proceedings
EditorsJiming Liu, Ning Zhong, Yiju Yao, Setsuo Ohsuga
PublisherSpringer Verlag
Pages579-583
Number of pages5
ISBN (Print)3540427309, 9783540427308
Publication statusPublished - 2001 Jan 1
Event1st Asia-Pacific Conference on Web Intelligence, WI 2001 - Maebashi City, Japan
Duration: 2001 Oct 232001 Oct 26

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2198
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other1st Asia-Pacific Conference on Web Intelligence, WI 2001
CountryJapan
CityMaebashi City
Period01-10-2301-10-26

Fingerprint

Self-organization
Visualization
Clustering
Silhouette
Appeal
Real-world Applications
Network Topology
User Interface
User interfaces
Paradigm
Integrate
Topology
Neural Networks
Neural networks
Coefficient

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Li, S-T. (2001). Leveraging a web-aware self-organization map tool for clustering and visualization. In J. Liu, N. Zhong, Y. Yao, & S. Ohsuga (Eds.), Web Intelligence: Research and Development - 1st Asia-Pacific Conference, WI 2001, Proceedings (pp. 579-583). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2198). Springer Verlag.
Li, Sheng-Tun. / Leveraging a web-aware self-organization map tool for clustering and visualization. Web Intelligence: Research and Development - 1st Asia-Pacific Conference, WI 2001, Proceedings. editor / Jiming Liu ; Ning Zhong ; Yiju Yao ; Setsuo Ohsuga. Springer Verlag, 2001. pp. 579-583 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{70ec6f5c6f6545b1b864dcc798692a17,
title = "Leveraging a web-aware self-organization map tool for clustering and visualization",
abstract = "The self-organization map (SOM) neural network has been recognized as a successful paradigm for clustering and visualization in a large variety of real-world applications. There exist a number of useful stand-alone SOM tools, however, they cannot be adapted to the new generation web environment. In addition, different user interfaces required for operation and the heterogeneity of platforms where the tools run on prevent them from appeal. In this paper, we propose a web-aware SOM tool which integrates the computationally powerful SOM PAK and the vivid Nenet tools to augment the advantages of each. The proposed SOM tool is capable of delimiting the desired clusters by adopting two level network topology and silhouette coefficients.",
author = "Sheng-Tun Li",
year = "2001",
month = "1",
day = "1",
language = "English",
isbn = "3540427309",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "579--583",
editor = "Jiming Liu and Ning Zhong and Yiju Yao and Setsuo Ohsuga",
booktitle = "Web Intelligence",
address = "Germany",

}

Li, S-T 2001, Leveraging a web-aware self-organization map tool for clustering and visualization. in J Liu, N Zhong, Y Yao & S Ohsuga (eds), Web Intelligence: Research and Development - 1st Asia-Pacific Conference, WI 2001, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2198, Springer Verlag, pp. 579-583, 1st Asia-Pacific Conference on Web Intelligence, WI 2001, Maebashi City, Japan, 01-10-23.

Leveraging a web-aware self-organization map tool for clustering and visualization. / Li, Sheng-Tun.

Web Intelligence: Research and Development - 1st Asia-Pacific Conference, WI 2001, Proceedings. ed. / Jiming Liu; Ning Zhong; Yiju Yao; Setsuo Ohsuga. Springer Verlag, 2001. p. 579-583 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2198).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Leveraging a web-aware self-organization map tool for clustering and visualization

AU - Li, Sheng-Tun

PY - 2001/1/1

Y1 - 2001/1/1

N2 - The self-organization map (SOM) neural network has been recognized as a successful paradigm for clustering and visualization in a large variety of real-world applications. There exist a number of useful stand-alone SOM tools, however, they cannot be adapted to the new generation web environment. In addition, different user interfaces required for operation and the heterogeneity of platforms where the tools run on prevent them from appeal. In this paper, we propose a web-aware SOM tool which integrates the computationally powerful SOM PAK and the vivid Nenet tools to augment the advantages of each. The proposed SOM tool is capable of delimiting the desired clusters by adopting two level network topology and silhouette coefficients.

AB - The self-organization map (SOM) neural network has been recognized as a successful paradigm for clustering and visualization in a large variety of real-world applications. There exist a number of useful stand-alone SOM tools, however, they cannot be adapted to the new generation web environment. In addition, different user interfaces required for operation and the heterogeneity of platforms where the tools run on prevent them from appeal. In this paper, we propose a web-aware SOM tool which integrates the computationally powerful SOM PAK and the vivid Nenet tools to augment the advantages of each. The proposed SOM tool is capable of delimiting the desired clusters by adopting two level network topology and silhouette coefficients.

UR - http://www.scopus.com/inward/record.url?scp=84942596881&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84942596881&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:84942596881

SN - 3540427309

SN - 9783540427308

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

SP - 579

EP - 583

BT - Web Intelligence

A2 - Liu, Jiming

A2 - Zhong, Ning

A2 - Yao, Yiju

A2 - Ohsuga, Setsuo

PB - Springer Verlag

ER -

Li S-T. Leveraging a web-aware self-organization map tool for clustering and visualization. In Liu J, Zhong N, Yao Y, Ohsuga S, editors, Web Intelligence: Research and Development - 1st Asia-Pacific Conference, WI 2001, Proceedings. Springer Verlag. 2001. p. 579-583. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).