Concept decompositions for large sparse text data using clustering
Machine Learning
Co-clustering documents and words using bipartite spectral graph partitioning
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Document clustering based on non-negative matrix factorization
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Soft clustering criterion functions for partitional document clustering: a summary of results
Proceedings of the thirteenth ACM international conference on Information and knowledge management
A Matrix Factorization Approach for Integrating Multiple Data Views
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Fuzzy semi-supervised co-clustering for text documents
Fuzzy Sets and Systems
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The primary goal of cluster analysis is to produce clusters that accurately reflect the natural groupings in the data. A second objective is to identify features that are descriptive of the clusters. In addition to these requirements, we often wish to allow objects to be associated with more than one cluster. In this paper we present a technique, based on the spectral co-clustering model, that is effective in meeting these objectives. Our evaluation on a range of text clustering problems shows that the proposed method yields accuracy superior to that afforded by existing techniques, while producing cluster descriptions that are amenable to human interpretation.