Simulated annealing: theory and applications
Simulated annealing: theory and applications
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Local Search in Combinatorial Optimization
Local Search in Combinatorial Optimization
A Monte Carlo algorithm for fast projective clustering
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
A Generic Framework for Efficient Subspace Clustering of High-Dimensional Data
ICDM '05 Proceedings of the Fifth IEEE 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
DUSC: Dimensionality Unbiased Subspace Clustering
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
INSCY: Indexing Subspace Clusters with In-Process-Removal of Redundancy
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
Detection of orthogonal concepts in subspaces of high dimensional data
Proceedings of the 18th ACM conference on Information and knowledge management
External evaluation measures for subspace clustering
Proceedings of the 20th ACM international conference on Information and knowledge management
Discovering Multiple Clustering Solutions: Grouping Objects in Different Views of the Data
ICDE '12 Proceedings of the 2012 IEEE 28th International Conference on Data Engineering
Multi-view clustering using mixture models in subspace projections
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
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Finding multiple clustering solutions has recently gained much attention. Based on the observation that data is often multi-faceted, novel clustering methods have been introduced capable of detecting multiple, diverse clusterings. In this work-in-progress paper, we present a novel stochastic subspace search principle that tackles the requirements of multi-view clustering. The main idea is to consider each subspace as a state in a Markov chain and using Monte Carlo methods to sample the multi-view subspaces. By dynamically adapting the underlying probability density function we realize the generation of alternative clustering views. We present preliminary experimental results of our method and we describe future research directions.