Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs
SIAM Journal on Scientific Computing
ACM Computing Surveys (CSUR)
Analyzing the effectiveness and applicability of co-training
Proceedings of the ninth international conference on Information and knowledge management
Combining Labeled and Unlabeled Data for Text Classification with a Large Number of Categories
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Combining Multiple Weak Clusterings
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Solving cluster ensemble problems by bipartite graph partitioning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Spectral clustering and transductive learning with multiple views
Proceedings of the 24th international conference on Machine learning
A multiview approach for intelligent data analysis based on data operators
Information Sciences: an International Journal
Tabu search for attribute reduction in rough set theory
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Probabilistic classification and clustering in relational data
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
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In various data mining applications performing the task of extracting information from large databases is serious problem, which occurs in many fields e.g.: bioinformatics, commercial behaviour of Internet users, social networks analysis, management and investigation of various databases in static or dynamic states. In recent years many techniques discovering hidden structures in the data set like clustering and projection of data from high-dimensional spaces have been developed. In this paper, we propose a model for multiple view unsupervised clustering based on Kohonen self-organizing-map algorithm. The results of simulations in two dimensional space using three views of training sets having different statistical properties have been presented.