제어로봇시스템학회:학술대회논문집

Institute of Control, Robotics and Systems (제어로봇시스템학회)
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High-Speed Self-Organzing Map for Document Clustering

  • Rojanavasu, Ponthap (Department of Computer Engineering, Faculty of Engineering King Mongkut’s Institute of Technology Ladkrabang) ;
  • Pinngern, Ouen (Department of Computer Engineering, Faculty of Engineering King Mongkut’s Institute of Technology Ladkrabang)
  • Published : 2003.10.22
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Abstract

Self-Oranizing Map(SOM) is an unsupervised neural network providing cluster analysis of high dimensional input data. The output from the SOM is represented in map that help us to explore data. The weak point of conventional SOM is when the map is large, it take a long time to train the data. The computing time is known to be O(MN) for trainning to find the winning node (M,N are the number of nodes in width and height of the map). This paper presents a new method to reduce the computing time by creating new map. Each node in a new map is the centroid of nodes' group that are in the original map. After create a new map, we find the winning node of this map, then find the winning node in original map only in nodes that are represented by the winning node from the new map. This new method is called "High Speed Self-Oranizing Map"(HS-SOM). Our experiment use HS-SOM to cluster documents and compare with SOM. The results from the experiment shows that HS-SOM can reduce computing time by 30%-50% over conventional SOM.

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