Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Information-theoretic co-clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Graph nodes clustering with the sigmoid commute-time kernel: A comparative study
Data & Knowledge Engineering
Clustering with Multiple Graphs
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
An Improved Co-Similarity Measure for Document Clustering
ICMLA '10 Proceedings of the 2010 Ninth International Conference on Machine Learning and Applications
Partitioning hard clustering algorithms based on multiple dissimilarity matrices
Pattern Recognition
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In this paper, we introduce the MVSim architecture which is able to cluster multi-view datasets (i.e. datasets containing several objects linked together by different relations), by using several instances of a co-similarity algorithm. We show that this framework provides better results than existing approaches, while reducing both time and space complexities thanks to an efficient parallelization of the computations. This approach allows to split large datasets into a set of smaller ones.