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@inproceedings{Bickel&Scheffer04,
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@article{journals/ida/SavaresiB04,
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title = {Wordnet improves text document clustering},
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author = {Zhao, Ying and Karypis, George},
title = {Evaluation of hierarchical clustering algorithms for document datasets},
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@inproceedings{658040,
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title = {Text Clustering Based on Good Aggregations},
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author = {Wagstaff, Kiri and Cardie, Claire and Rogers, Seth and Schrödl, Stefan},
title = {Constrained K-means Clustering with Background Knowledge.},
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url = {http://www.litech.org/~wkiri/Papers/wagstaff-kmeans-01.pdf},
keywords = {clustering, constraints, kmeans}
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author = {Kirsten, Mathias and Wrobel, Stefan},
title = {Extending K-Means Clustering to First-Order Representations},
editor = {Cussens, James and Frisch, Alan M.},
booktitle = {Inductive Logic Programming, 10th International Conference, ILP 2000, London, UK, July 24-27, 2000, Proceedings},
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volume = {1866},
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title = {$X$-means: Extending $K$-means with Efficient Estimation of the Number of Clusters},
booktitle = {Proc. 17th International Conf. on Machine Learning},
publisher = {Morgan Kaufmann, San Francisco, CA},
year = {2000},
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keywords = {clustering, kmeans}
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booktitle = {KDD Workshop on Text Mining},
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url = {http://citeseer.ist.psu.edu/steinbach00comparison.html},
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year = {1999},
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publisher = {Morgan Kaufmann, San Francisco, CA},
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keywords = {clustering, kmeans}
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@book{hartigan75,
author = {Hartigan, J.},
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keywords = {clustering, kmeans}
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author = {MacQueen, J. B.},
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booktitle = {Proc. of the fifth Berkeley Symposium on Mathematical Statistics and Probability},
publisher = {University of California Press},
year = {1967},
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keywords = {kmeans, clustering}
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@techreport{ballhall65,
author = {Ball, G. and Hall, D.},
title = {ISODATA: A novel method of data analysis and pattern classification},
address = {Menlo Park},
year = {1965},
keywords = {clustering, kmeans}
}
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@article{forgy65,
author = {Forgy, E.},
title = {Cluster Analysis of Multivariate Data: Efficiency versus Interpretability of Classification},
journal = {Biometrics},
year = {1965},
volume = {21},
number = {3},
pages = {768-769},
keywords = {clustering, kmeans}
}