Interactive Visual Clustering and Classification based on Dimensionality Reduction Mappings: A Case Study for Analyzing Patients with Dermatologic Conditions

Abstract

Multidimensional data sets are becoming more frequent in practically all research fields, and require complex data analysis techniques in order to extract knowledge from them. In this paper, we propose an interactive visualization tool for performing exploratory data analysis. The tool combines unsupervised and supervised dimensionality reduction methods, such as linear discriminant analysis, or t-SNE, with clustering and classification techniques. Analysts can use several machine learning methods for extracting data structure, and can group data into clusters interactively or through clustering algorithms. In addition they can visualize projections of the data to evaluate the quality of obtained clusters, and to analyze the performance of classification methods. We have applied this tool to analyze a clinical data set related to patients with dermatologic conditions that are under photodynamic therapy. The analysis allowed medical doctors to identify several clinically interesting patient groups. In addition, clinicians discovered a greater efficacy in the treatment of patients with the photosensitizer 5-aminolaevulinic acid nanoemulsion gel compared to those treated with methyl-5-aminolaevulinate cream.