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

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Abstract

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.

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Authors and Affiliations

  1. Clayton School of Information Technology, Monash University, Clayton, VIC, 3800, Australia

    Norashikin Ahmad, Damminda Alahakoon & Rowena Chau

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  1. Norashikin Ahmad
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  2. Damminda Alahakoon
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  3. Rowena Chau
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Correspondence to Norashikin Ahmad.

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Ahmad, N., Alahakoon, D. & Chau, R. Cluster identification and separation in the growing self-organizing map: application in protein sequence classification. Neural Comput & Applic 19, 531–542 (2010). https://doi.org/10.1007/s00521-009-0300-0

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  • DOI: https://doi.org/10.1007/s00521-009-0300-0

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