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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)
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)
Information extraction is concerned with the location of specific items in (unstructured) textual documents, e.g., being applied for the acquisition of structured data. Then, the acquired data can be applied for mining methods requiring structured input data, in contrast to other text mining methods that utilize a bag-of-words approach. This paper presents(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)