Visualized mixed-type data analysis via dimensionality reduction

Abstract

Visualization is a useful technique in data analysis, especially, in the initial stage, data exploration. Since high-dimensional data is not visible, dimensionality reduction techniques are usually used to reduce the data to a lower dimension, say two, for visualization. In previous studies, dimensionality reduction was investigated in the context of numeric datasets. Nevertheless, most of real-world datasets are of mixed-type containing both numeric and categorical attributes. In this case, a traditional approach could neither handle it directly nor output appropriate results. To address this problem, we propose a procedure for visualized analysis of mixed-type data via dimensionality reduction. Dissimilarity between categorical values is learned from the dataset and further used to measure the distance between mixed-type data points. In addition, we propose an approach to identifying significant features and visualizing patterns from the projection map chosen according to quality measures. Experiments on real-world datasets were conducted to demonstrate feasibility of the proposed method.