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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)
Subgroup discovery is a broadly applicable descriptive data mining technique for identifying interesting subgroups according to some property of interest. This article summarizes fundamentals of subgroup discovery, before it reviews algorithms and further advanced methodological issues. In addition, we briefly discuss tools and applications of subgroup(More)
We present a new method for detecting descriptive community patterns capturing exceptional (sequential) link trails. For that, we provide a novel problem formalization: We model sequential data as first-order Markov chain models, mapped to an attributed weighted network represented as a graph. Then, we detect subgraphs (communities) using exceptional model(More)
Subgroup discovery is a key data mining method that aims at identifying descriptions of subsets of the data that show an interesting distribution with respect to a pre-defined target concept. For practical applications the integration of numerical data is crucial. Therefore, a wide variety of interestingness measures has been proposed in literature that use(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)
The result of data mining is a set of patterns or models. When presenting these, all or part of the result needs to be explained to the user in order to be understandable and for increasing the user acceptance of the patterns. In doing that, a variety of dimensions for explaining needs to be considered, e.g., from concrete to more abstract explanations.(More)
Exceptional model mining has been proposed as a variant of subgroup discovery especially focusing on complex target concepts. Currently, efficient mining algorithms are limited to heuristic (non exhaustive) methods. In this paper, we propose a novel approach for fast exhaustive exceptional model mining: We introduce the concept of valuation bases as an(More)