Model-based overlapping clustering
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Non-redundant Multi-view Clustering via Orthogonalization
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Simultaneous Unsupervised Learning of Disparate Clusterings
Statistical Analysis and Data Mining
ACM Transactions on Knowledge Discovery from Data (TKDD)
Multiplicative Mixture Models for Overlapping Clustering
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
A principled and flexible framework for finding alternative clusterings
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Adapting the right measures for K-means clustering
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Detection of orthogonal concepts in subspaces of high dimensional data
Proceedings of the 18th ACM conference on Information and knowledge management
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Mixture models for learning low-dimensional roles in high-dimensional data
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
External evaluation measures for subspace clustering
Proceedings of the 20th ACM international conference on Information and knowledge management
Stochastic subspace search for top-k multi-view clustering
Proceedings of the 4th MultiClust Workshop on Multiple Clusterings, Multi-view Data, and Multi-source Knowledge-driven Clustering
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Detecting multiple clustering solutions is an emerging research field. While data is often multi-faceted in its very nature, traditional clustering methods are restricted to find just a single grouping. To overcome this limitation, methods aiming at the detection of alternative and multiple clustering solutions have been proposed. In this work, we present a Bayesian framework to tackle the problem of multi-view clustering. We provide multiple generalizations of the data by using multiple mixture models. Each mixture describes a specific view on the data by using a mixture of Beta distributions in subspace projections. Since a mixture summarizes the clusters located in similar subspace projections, each view highlights specific aspects of the data. In addition, our model handles overlapping views, where the mixture components compete against each other in the data generation process. For efficiently learning the distributions, we propose the algorithm MVGen that exploits the ICM principle and uses Bayesian model selection to trade-off the cluster model's complexity against its goodness of fit. With experiments on various real-world data sets, we demonstrate the high potential of MVGen to detect multiple, overlapping clustering views in subspace projections of the data.