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Today's applications deal with multiple types of information: graph data to represent the relations between objects and attribute data to characterize single objects. Analyzing both data sources simultaneously can increase the quality of mining methods. Recently, combined clustering approaches were introduced, which detect densely connected node sets within one large graph that also show high similarity...
Large amounts of data are ubiquitous today. Data mining methods like clustering were introduced to gain knowledge from these data. Recently, detection of multiple clusterings has become an active research area, where several alternative clustering solutions are generated for a single dataset. Each of the obtained clustering solutions is valid, of importance, and provides a different interpretation...
Subspace clustering aims at detecting clusters in any subspace projection of a high dimensional space. As the number of possible subspace projections is exponential in the number of dimensions, the result is often tremendously large. Recent approaches fail to reduce results to relevant subspace clusters. Their results are typically highly redundant, i.e. many clusters are detected multiple times in...
Subspace clustering aims at detecting clusters in any subspace projection of a high dimensional space. As the number of projections is exponential in the number of dimensions, efficiency is crucial. Moreover, the resulting subspace clusters are often highly redundant, i.e. many clusters are detected multiply in several projections. We propose a novel index for efficient subspace clustering in a novel...
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