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Cluster identification and separation in the growing self-organizing map: application in protein sequence classification

Ahmad, Norashikin, Alahakoon, Damminda and Chau, Rowena 2010, Cluster identification and separation in the growing self-organizing map: application in protein sequence classification, Neural computing and applications, vol. 19, no. 4, pp. 531-542, doi: 10.1007/s00521-009-0300-0.

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Title Cluster identification and separation in the growing self-organizing map: application in protein sequence classification
Author(s) Ahmad, Norashikin
Alahakoon, Damminda
Chau, Rowena
Journal name Neural computing and applications
Volume number 19
Issue number 4
Start page 531
End page 542
Total pages 12
Publisher Springer-Verlag
Place of publication Berlin, Germany
Publication date 2010-06
ISSN 1433-3058
Keyword(s) Cluster identification
cluster separation
unsupervised neural networks
dynamic self-organizing map
protein sequence classification
Summary Growing self-organizing map (GSOM) has been introduced as an improvement to the self-organizing map (SOM) algorithm in clustering and knowledge discovery. Unlike the traditional SOM, GSOM has a dynamic structure which allows nodes to grow reflecting the knowledge discovered from the input data as learning progresses. The spread factor parameter (SF) in GSOM can be utilized to control the spread of the map, thus giving an analyst a flexibility to examine the clusters at different granularities. Although GSOM has been applied in various areas and has been proven effective in knowledge discovery tasks, no comprehensive study has been done on the effect of the spread factor parameter value to the cluster formation and separation. Therefore, the aim of this paper is to investigate the effect of the spread factor value towards cluster separation in the GSOM. We used simple k-means algorithm as a method to identify clusters in the GSOM. By using Davies–Bouldin index, clusters formed by different values of spread factor are obtained and the resulting clusters are analyzed. In this work, we show that clusters can be more separated when the spread factor value is increased. Hierarchical clusters can then be constructed by mapping the GSOM clusters at different spread factor values.
Language eng
DOI 10.1007/s00521-009-0300-0
Field of Research 109999 Technology not elsewhere classified
Socio Economic Objective 970110 Expanding Knowledge in Technology
HERDC Research category C1.1 Refereed article in a scholarly journal
Copyright notice ©2010, Springer
Persistent URL http://hdl.handle.net/10536/DRO/DU:30064344

Document type: Journal Article
Collection: School of Information and Business Analytics
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Citation counts: TR Web of Science Citation Count  Cited 11 times in TR Web of Science
Scopus Citation Count Cited 12 times in Scopus
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