From Local Patterns to Global Models

Concept description is an important task of descriptive data mining: Basically, its aim is to identify and to summarize properties of a selected target population in the form of a set of patterns – in a concise and comprehensible way. In this paper we present an approach for concept description in the social bookmarking domain: We show how subgroup discovery can be utilized for identifying discriminative and characteristic local patterns in order to understand the behavior of (non-)spammers. A case study applying data from a real-world system for social bookmarking provides exemplary results and demonstrates the applicability and effectiveness of the presented approach.