Neural Computing and Applications

, Volume 19, Issue 4, pp 531–542 | Cite as

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

  • Norashikin AhmadEmail author
  • Damminda Alahakoon
  • Rowena Chau
Original Article


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.


Cluster identification Cluster separation Unsupervised neural networks Dynamic self-organizing map Protein sequence classification 


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Copyright information

© Springer-Verlag London Limited 2009

Authors and Affiliations

  • Norashikin Ahmad
    • 1
    Email author
  • Damminda Alahakoon
    • 1
  • Rowena Chau
    • 1
  1. 1.Clayton School of Information TechnologyMonash UniversityClaytonAustralia

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