Concept hierarchy in data mining : specification, generation and implementation

Abstract

Data mining is the nontrivial extraction of implicit, previously unknown, and potentially useful information from data. As one of the most important background knowledge, concept hierarchy plays a fundamentally important role in data mining. It is the purpose of this thesis to study some aspects of concept hierarchy such as the automatic generation and encoding technique in the context of data mining. After the discussion on the basic terminology and categorization, automatic generation of concept hierarchies is studied for both nominal and numerical hierarchies. One algorithm is designed for determining the partial order on a given set of nominal attributes. The resulting partial order is a useful guide for users to finalize the concept hierarchy for their particular data mining tasks. Based on hierarchical and partitioning clustering methods, two algorithms are proposed for the automatic generation of numerical hierarchies. The quality and performance comparisons indicates that the ...