Unsupervised Learning from Categorical Data:A Space Transformation Approach
Wang Jian-xi · 2016
The unsupervised learning method of categorical data plays a more and more important role in such areas as pattern recognition,machine learning,data mining and knowledge discovery in the recent years.Nevertheless,in view of many existing clustering algorithms for categorical data(the classical k-modes algorithm and so on),there is still a large room for improving their clustering performance in comparison with the performance of clustering algorithms for numeric data.This may arise from the fact that categorical data lack a clear space structure as that of numeric data.To effectively discover the space structure inherent in a set of categorical objects,we adopted a novel data representation scheme:a space transformation approach,which maps a set of categorical objects into a corresponding Euclidean space with the new dimensions constructed by each of the original features.Based on the new general framework for categorical clustering,we employed the Carreira-Perpin's K-modes algorithm for clustering to find more representative modes.The performance of the new proposed method was tested on the nine frequently-used categorical data sets downloaded from the UCI.Comparisons with the traditional clustering algorithms for categorical data illustrate the effectiveness of the new method on almost all data sets.