Development of ICASOM enabling a high-level clustering

Abstract

Recently, SOM (Self-Organizing Map) is noticed as one of the analysis techniques of multi-dimension data, and it is actively studied and applied in various fields, such as medical treatment and environment. In clustering by the conventional SOM, data is classified according to compressing the inputted multi-dimension data into two dimensions. However, information on the cause of existing inside data cannot be analyzed. Therefore, ICA (Independent Component Analysis) is taken in to the conventional SOM, data is decomposed into some components, and the extraction and analysis of an independent component having contained the amount of the characteristics of input data are conducted. As a result, we thought that it enables a high level clustering which solved the above-mentioned problem. In this research, we develop the analysis technique (ICASOM) which combines SOM and ICA, and prove the effectiveness by applying this method to the acceleration plethysmogram.