A flexible multi-layer self-organizing map for generic processing of tree-structured data

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

21 Scopus Citations
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Detail(s)

Original languageEnglish
Pages (from-to)1406-1424
Journal / PublicationPattern Recognition
Volume40
Issue number5
Publication statusPublished - May 2007

Abstract

A new multi-layer self-organizing map (MLSOM) is proposed for unsupervised processing tree-structured data. The MLSOM is an improved self-organizing map for handling structured data. By introducing multiple SOM layers, the MLSOM can overcome the computational speed and visualization problems of SOM for structured data (SOM-SD). Node data in different levels of a tree are processed in different layers of the MLSOM. Root nodes are dedicatedly processed on the top SOM layer enabling the MLSOM a better utilization of SOM map compared with the SOM-SD. Thus, the MLSOM exhibits better data organization, clustering, visualization, and classification results of tree-structured data. Experimental results on three different data sets demonstrate that the proposed MLSOM approach can be more efficient and effective than the SOM-SD. © 2006 Pattern Recognition Society.

Research Area(s)

  • Multi-layer self-organizing map (MLSOM), Self-organizing map (SOM), Tree-structured data

Citation Format(s)

A flexible multi-layer self-organizing map for generic processing of tree-structured data. / Rahman, M. K M; Pi Yang, Wang; Chow, Tommy W.S. et al.
In: Pattern Recognition, Vol. 40, No. 5, 05.2007, p. 1406-1424.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review