c. Text Data Clustering / Document Clustering

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  2. Zhao, Y., G. Karypis, and U. Fayyad, Hierarchical Clustering Algorithms for Document Datasets. Data Mining and Knowledge Discovery, 2005. 10(2): p. 141-168.
  3. Xu, W., X. Liu, and Y. Gong, Document clustering based on non-negative matrix factorization, in Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval2003, ACM: Toronto, Canada. p. 267-273.
  4. Hotho, A., S. Staab, and G. Stumme. Ontologies improve text document clustering. in Data Mining, 2003. ICDM 2003. Third IEEE International Conference on. 2003.
  5. Fung, B.C.M., K. Wang, and M. Ester. Hierarchical document clustering using frequent itemsets. 2003.
  6. Beil, F., M. Ester, and X. Xu, Frequent term-based text clustering, in Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining2002, ACM: Edmonton, Alberta, Canada. p. 436-442.
  7. Steinbach, M., G. Karypis, and V. Kumar. A comparison of document clustering techniques. 2000. Boston.
  8. Slonim, N. and N. Tishby, Document clustering using word clusters via the information bottleneck method, in Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval2000, ACM: Athens, Greece. p. 208-215.
  9. Zamir, O. and O. Etzioni, Web document clustering: a feasibility demonstration, in Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval1998, ACM: Melbourne, Australia. p. 46-54.
  10. Peter, W., Recent trends in hierarchic document clustering: A critical review. Information Processing & Management, 1988. 24(5): p. 577-597.
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