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

M. K M Rahman, Wang Pi Yang, Tommy W.S. Chow, Sitao Wu

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

23 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)1406-1424
JournalPattern Recognition
Volume40
Issue number5
DOIs
Publication statusPublished - May 2007

Research Keywords

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

Fingerprint

Dive into the research topics of 'A flexible multi-layer self-organizing map for generic processing of tree-structured data'. Together they form a unique fingerprint.

Cite this