MAPPING METHOD OF PHARMACEUTICAL COMPOUNDS BY TOPOLOGICAL DATA ANALYSIS BASED ON COSA DISSIMILARITIES

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, resultin...

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Bibliographic Details
Published inBulletin of the Computational Statistics of Japan Vol. 35; no. 2; pp. 49 - 67
Main Authors Kitanishi, Yoshitake, Ishioka, Fumio, Iizuka, Masaya, Kurihara, Koji
Format Journal Article
LanguageJapanese
Published Japanese Society of Computational Statistics 2022
Subjects
Chemical clustering
Mapping
Subsets of variables
Topological approach
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Summary: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.
ISSN:0914-8930
2189-9789
DOI:10.20551/jscswabun.35.2_49