Improved SOM-Based High-Dimensional Data Visualization Algorithm


  •  Wang Zhisheng    
  •  Xu Xiaobing    

Abstract

In this paper, a new high-dimensional data visualization algorithm based on the Self-Organizing Map (SOM) is proposed. It is named TDSOM (three-dimensional self-organizing map) to describe its special characteristics. TDSOM trains the high-dimensional data with SOM network and projects it into particular point sets in the three-dimensional coordinate system. In the three-dimensional coordinate system, the x axis represents attributes of the original data set; the y axis represents the weight of each attribute; the z axis represents different categories of the mapping result. The most important is that researchers can watch the three-dimensional model from different viewpoints by rotating it and gain some interesting patterns. Through the experiment, TDSOM is proved to be much more accurate and more analytical than the traditional methods in displaying the high-dimensional data. The main innovation of the new TDSOM algorithm is the presentation of large data in three-dimensional coordinate system which provides a much wider view than the two-dimensional one. What’s more, users are able to discover some interesting patterns according to their own research areas through the model. The algorithm can be widely applied in areas such as data mining, pattern recognition and so on.


This work is licensed under a Creative Commons Attribution 4.0 License.
  • ISSN(Print): 1913-8989
  • ISSN(Online): 1913-8997
  • Started: 2008
  • Frequency: quarterly

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