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This paper focuses on the community analysis of conference participants using their face-to-face contacts, visited talks, and tracks in a social and ubiquitous conferencing scenario. We consider human face-to-face contacts and perform a dynamic analysis of the number of contacts and their lengths. On these dimensions, we specifically investigate(More)
In general, knowledge-intensive data mining methods exploit background knowledge to improve the quality of their results. Then, in knowledge-rich domains often the interestingness of the mined patterns can be increased significantly. In this paper we categorize several classes of background knowledge for subgroup discovery, and present how the necessary(More)
Conferator is a novel social conference system that provides the management of social interactions and context information in ubiquitous and social environments. Using RFID and social networking technology, Conferator provides the means for effective management of personal contacts and according conference information before, during and after a conference.(More)
Subgroup discovery is a prominent data mining method for discovering local patterns. Since often a set of very similar , overlapping subgroup patterns is retrieved, efficient methods for extracting a set of relevant subgroups are required. This paper presents a novel algorithm based on a vertical data structure, that not only discovers interesting subgroups(More)
In general, cases capture knowledge and concrete experiences of specific situations. By exploiting case-based knowledge for characterizing a subgroup pattern, additional information about the subgroup objects can be provided. This paper proposes a case-based approach for characterizing and analyzing subgroup patterns: It presents techniques for retrieving(More)