Feature Space Decomposition using Information Theory

This article discusses the decomposition of an object space described by a large number of binary and ordinal features. It is assumed that no class labels are known and assigned to objects. The proposed method is a combination of clustering and community detection methods and allows for the construction of overlapping clusters. Subsets of relevant features are detected using redundant information. Feature dependency measures are based on information theory tools. Achieved results are demonstrated on data sets characterizing officials in the Old Kingdom of ancient Egypt.