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Hierarchical Divisive Clustering with Multi View-Point Based Similarity Measure

  • S. Jayaprada
  • Amarapini Aswani
  • G. Gayathri
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 247)

Abstract

Clustering is task of grouping a set of objects in such a way that objects in the same group are more similar to each other than to those in other groups. In this paper, we introduce hierarchical divisive clustering with multi view point based similarity measure. The hierarchical clustering is produced by the sequence of repeated bisections. The bisecting incremental k-means with multi view point based similarity measure is used in the clustering. We compare our approach with the existing algorithms on various document collections to verify the advantage of our proposed method.

Keywords

Hierarchical Clustering Document Clustering Text Mining Similarity Measure 

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References

  1. 1.
    Nguyen, D.T., Chen, L., Chan, C.K.: Clustering with Multi-Viewpoint Based Similarity Measure. IEEE Transactions on Knowledge and Data Engineering PP (2011)Google Scholar
  2. 2.
    Wu, X., Kumar, V., Quinlan, J.R., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G.J., Ng, A., Liu, B., Yu, P.S., Zhou, Z.-H., Steinbach, M., Hand, D.J., Steinberg, D.: Top 10 Algorithms in Data Mining. Knowledge Information Systems 14(1), 1–37 (2007)CrossRefGoogle Scholar
  3. 3.
    Steinbach, M., Karypis, G., Kumar, V.: A Comparison of Document Clustering Techniques. In: KDD Workshop on Text Mining (2000)Google Scholar
  4. 4.
    Manning, C.D., Raghavan, P., Schutze, H.: An Introduction to Information Retrieval. Cambridge Univ. Press (2009)Google Scholar
  5. 5.
    Dhillon, Modh, D.: Concept Decompositions for Large Sparse Text Data Using Clustering. Machine Learning 42(1/2), 143–175 (2001)CrossRefMATHGoogle Scholar
  6. 6.
    Zhao, Y., Karypis, G.: Empirical and Theoretical Comparisons of Selected Criterion Functions for Document Clustering. Machine Learning 55(3), 311–331 (2004)CrossRefMATHGoogle Scholar
  7. 7.
    Karypis, G.: CLUTO a Clustering Toolkit. technical report, Dept. of Computer Science, Univ. of Minnesota (2003), http://glaros.dtc.umn.edu/~gkhome/views/cluto
  8. 8.
    Zhong, S., Ghosh, J.: A Comparative Study of Generative Models for Document Clustering. In: Proc. SIAM Int’l Conf. Data Mining Workshop Clustering High Dimensional Data and its Applications (2003)Google Scholar
  9. 9.
    Zhao, Y., Karypis, G.: Criterion Functions for Document Clustering: Experiments and Analysis. Technical Report, Dept. of Computer Science, Univ. of Minnesota (2002)Google Scholar
  10. 10.
    Zhao, Y., Karypis, G.: Evaluation of hierarchical clustering algorithms for document datasets. In: Proc. of Int. Conf. on Inf. & Knowledge Management, pp. 515–524 (2002)Google Scholar
  11. 11.
    Huang, A.: Similarity Measures for Text Document Clustering. In: NZCSRSC 2008, Christchurch, New Zealand (April 2008)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringAnil Neerukonda Institute of Technology and SciencesVisakhapatnamIndia

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