Sparse Generative Topographic Mapping for Both Data Visualization and Clustering

• Published in: Journal of Chemical Information and Modeling Vol. 58; no. 12; pp. 2528 - 2535
• Main Author: Kaneko, Hiromasa
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 24.12.2018; American Chemical Society (ACS)
• Subjects: Algorithms; Artificial intelligence; Bayesian analysis; Cluster Analysis; Clustering; Computer simulation; Data points; Data Visualization; Databases, Chemical; Datasets; Environmental Pollutants; Geography; Machine Learning; Mapping; Quantitative Structure-Activity Relationship; Scientific visualization; Solubility; Topographic mapping; Topography; Visualization; Weight
• ISSN: 1549-9596; 1549-960X; 1549-960X
• DOI: 10.1021/acs.jcim.8b00528

To achieve simultaneous data visualization and clustering, the method of sparse generative topographic mapping (SGTM) is developed by modifying the conventional GTM algorithm. While the weight of each grid point is constant in the original GTM, it becomes a variable in the proposed SGTM, enabling data points to be clustered on two-dimensional maps. The appropriate number of clusters is determined by optimization based on the Bayesian information criterion. Analysis of numerical simulation data sets along with quantitative structure–property relationship and quantitative structure–activity relationship data sets confirmed that the proposed SGTM provides the same degree of visualization performance as the original GTM and clusters data points appropriately. Python and MATLAB codes for the proposed algorithm are available at https://github.com/hkaneko1985/gtm-generativetopographicmapping.