Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Evaluation of hierarchical clustering algorithms for document datasets
Proceedings of the eleventh international conference on Information and knowledge management
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
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ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
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Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
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Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
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Proceedings of the thirteenth ACM international conference on Information and knowledge management
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
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Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
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The biases of individual algorithms for non-parametric document clustering can lead to non-optimal solutions. Ensemble clustering methods may overcome this limitation, but have not been applied to document collections. This paper presents a comparison of strategies for non-parametric document ensemble clustering.