Grouping related attributes

Grouping objects that are described by attributes, or clustering is a central notion in data mining. On the other hand, similarity or relationships between at-tributes themselves is equally important but relatively unexplored. Such groups of attributes are also known as directories, concept hierarchies or topics depending on the underlying data domain. The similarities between the two problems of grouping objects and attributes might suggest that traditional clustering techniques are ap-plicable. This thesis argues that traditional clustering techniques fail to adequately capture the solution we seek. It also explores domain-independent techniques for grouping attributes. The notion of similarity between attributes and therefore clustering in cat-egorical datasets has not received adequate attention. This issue has seen renewed interest in the knowledge discovery community, spurred on by the requirements of personalization of information and online search technology. The problem is broken down into (a) quantification of this notion of similarity and (b) the subsequent formation of groups, retaining attributes similar enough i