An extension of the PMML standard to subspace clustering models
Proceedings of the 2011 workshop on Predictive markup language modeling
Designing an ensemble classifier over subspace classifiers using iterative convergence routine
Proceedings of the 20th ACM international conference on Information and knowledge management
Clouding services for linked data exploration
CAiSE'12 Proceedings of the 24th international conference on Advanced Information Systems Engineering
Outlier ensembles: position paper
ACM SIGKDD Explorations Newsletter
Ensembles for unsupervised outlier detection: challenges and research questions a position paper
ACM SIGKDD Explorations Newsletter
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Traditional clustering algorithms identify just a single clustering of the data. Today's complex data, however, allow multiple interpretations leading to several valid groupings hidden in different views of the database. Each of these multiple clustering solutions is valuable and interesting as different perspectives on the same data and several meaningful groupings for each object are given. Especially for high dimensional data where each object is described by multiple attributes, alternative clusters in different attribute subsets are of major interest. In this tutorial, we describe several real world application scenarios for multiple clustering solutions. We abstract from these scenarios and provide the general challenges in this emerging research area. We describe state-of-the-art paradigms, we highlight specific techniques, and we give an overview of this topic by providing a taxonomy of the existing methods. By focusing on open challenges, we try to attract young researchers for participating in this emerging research field.