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Document Clustering

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Encyclopedia of Database Systems
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Synonyms

Text clustering; High-dimensional clustering; Unsupervised learning on document datasets

Definition

At a high-level the problem of document clustering is defined as follows. Given a set S of n documents, we would like to partition them into a pre-determined number of k subsets S 1, S 2,...,S k , such that the documents assigned to each subset are more similar to each other than the documents assigned to different subsets. Document clustering is an essential part of text mining and has many applications in information retrieval and knowledge management. Document clustering faces two big challenges: the dimensionality of the feature space tends to be high (i.e., a document collection often consists of thousands or tens of thousands unique words); the size of a document collection tends to be large.

Historical Background

Fast and high-quality document clustering algorithms play an important role in providing intuitive navigation and browsing mechanisms as well as in facilitating...

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Recommended Reading

  1. Boley D. Principal direction divisive partitioning. Data Mining Knowl. Discov., 2(4), 1998.

    Google Scholar 

  2. Cutting D.R., Pedersen J.O., Karger D.R., and Tukey J.W. Scatter/gather: A cluster-based approach to browsing large document collections. In Proc. 15th Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, 1992, pp. 318–329.

    Google Scholar 

  3. Dempster A.P., Laird N.M., and Rubin D.B. Maximum likelihood from incomplete data via the em algorithm. J. R. Stat. Soc., 39, 1977.

    Google Scholar 

  4. Dhillon I.S. Co-clustering documents and words using bipartite spectral graph partitioning. In Knowledge Discovery and Data Mining, 2001, pp. 269–274.

    Google Scholar 

  5. Ding C., He X., Zha H., Gu M., and Simon H. 1Spectral min-max cut for graph partitioning and data clustering. Technical Report TR-2001-XX, Lawrence Berkeley National Laboratory, University of California, Berkeley, CA, 2001.

    Google Scholar 

  6. Duda R.O., Hart P.E., and Stork D.G. Pattern Classification. Wiley, New York, 2001.

    Google Scholar 

  7. Fisher D. Iterative optimization and simplification of hierarchical clusterings. J. Artif. Intell. Res., 4:147–180, 1996.

    MATH  Google Scholar 

  8. Jain A.K. and Dubes R.C. Algorithms for Clustering Data. Prentice Hall, New York, 1988.

    MATH  Google Scholar 

  9. Karypis G. Cluto: A clustering toolkit. Technical Report 02-017, Department of Computer Science, University of Minnesota, 2002.

    Google Scholar 

  10. King B. Step-wise clustering procedures. J. Am. Stat. Assoc., 69:86–101, 1967.

    Google Scholar 

  11. MacQueen J. Some methods for classification and analysis of multivariate observations. In Proc. 5th Symp. Math. Stat. Prob., 1967, pp. 281–297.

    Google Scholar 

  12. Salton G. Automatic Text Processing: The Transformation, Analysis, and Retrieval of Information by Computer. Addison-Wesley, Reading, MA, 1989.

    Google Scholar 

  13. Sneath P.H. and Sokal R.R. Numerical Taxonomy. Freeman, London, UK, 1973.

    MATH  Google Scholar 

  14. Zahn K. Graph-tehoretical methods for detecting and describing gestalt clusters. IEEE Trans. Comput., (C-20):68–86, 1971.

    Google Scholar 

  15. Zha H., He X., Ding C., Simon H., and Gu M. Bipartite graph partitioning and data clustering. In Proc. Int. Conf. on Information and Knowledge Management, 2001.

    Google Scholar 

  16. Zhao Y. and Karypis G. Criterion functions for document clustering: Experiments and analysis. Mach. Learn. 55:311–331, 2004.

    MATH  Google Scholar 

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Zhao, Y., Karypis, G. (2009). Document Clustering. In: LIU, L., ÖZSU, M.T. (eds) Encyclopedia of Database Systems. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-39940-9_1479

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