Abstract
Recent years have witnessed the accumulation of vast amounts of complicated data and information. Classification and visualisation of these data are important as the first step of analysis. However, in the conventional general clustering method, all attribute information is handled equally, resulting in noise and obscuring the true structure. Another issue is how to spatially capture the characteristics of the data and robustly visualise the update and increase of the data. To solve these problems, this paper proposes the combination method of Clustering Objects on Subsets of Attributes (COSA) which captures attribute information as a subset and calculates a distance matrix, and a topological data analysis mapper (TDA Mapper) that visualises complex data structures as shapes. Furthermore, we confirm its effectiveness with extended data based on the iris data, and an application example for mapping drug data is shown.
