
Improved SOMBased HighDimensional Data Visualization AlgorithmDOI: 10.5539/cis.v5n4p110 Abstract: In this paper, a new highdimensional data visualization algorithm based on the SelfOrganizing Map (SOM) is proposed. It is named TDSOM (threedimensional selforganizing map) to describe its special characteristics. TDSOM trains the highdimensional data with SOM network and projects it into particular point sets in the threedimensional coordinate system. In the threedimensional 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 threedimensional 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 highdimensional data. The main innovation of the new TDSOM algorithm is the presentation of large data in threedimensional coordinate system which provides a much wider view than the twodimensional 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.
