VISUAL ANALYSIS AND PROCESSING OF CLUSTERS STRUCTURES IN MULTIDIMENSIONAL DATASETS

Abstract

Abstract. The article is devoted to problems of visual analysis of clusters structures for a multidimensional datasets. For visual analyzing an approach of elastic maps design [1,2] is applied. This approach is quite suitable for processing and visualizing of multidimensional datasets. To analyze clusters in original data volume the elastic maps are used as the methods of original data points mapping to enclosed manifolds having less dimensionality. Diminishing the elasticity parameters one can design map surface which approximates the multidimensional dataset in question much better. Then the points of dataset in question are projected to the map. The extension of designed map to a flat plane allows one to get an insight about the cluster structure of multidimensional dataset. The approach of elastic maps does not require any a priori information about data in question and does not depend on data nature, data origin, etc. Elastic maps are usually combined with PCA approach. Being presented in the space based on three first principal components the elastic maps provide quite good results. The article describes the results of elastic maps approach application to visual analysis of clusters for different multidimensional datasets including medical data.