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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2))

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Abstract

The hierarchical clustering is an important method of clustering analysis. This kind of method can decompose the data into different levels, and the clustering result has a hierarchical coarseness to fine representation characteristic. In this paper, a new hierarchical clustering method based on GiST is proposed, which could store the structure of the tree generated during the clustering procedure in the hard disk. So it can support very detail analyzing procedure. The users can discover the relationship among clusters conveniently with this method.

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References

  1. Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann Publisher, San Francisco California (2001)

    Google Scholar 

  2. Zhang, T., Ramakrishnan, R., Livny, M., BIRCH,: An Efficient Data Clustering Method for Very Large Databases. In: Proceedings of the ACM SIGMOD Conference on Management of Data, Montreal, Canada, pp. 103–114. ACM Press, New York (1996)

    Google Scholar 

  3. Guha, U., Rastogi, R., Shim, K.: CURE,: an efficient clustering algorithm for large databases. Pergamon Information Systems 26, 35–58 (2001)

    Article  MATH  Google Scholar 

  4. Karypis, G., Han, E., Kumar, V.: CHAMELEON: A Hierarchical Clustering Algorithm Using Dynamic Modeling. COMPUTER 32, 68–75 (1999)

    Article  Google Scholar 

  5. Joseph, M., Hellerstein, Jeffrey, Naughton, F., Pfeffer, A.: A Generalized Search Trees for Database System. In: Proc. of the 21th Very Large Data Base Conference. Zurich Switzerland, pp. 562–573 (1995)

    Google Scholar 

  6. Zhou, B., Shen, J., Peng, Q.: Clustering Algorithm Based on Random-Sampling and Cluster-Feature. Journal Of Xi’an Jiaoton University 37, 1234–1237 (2003)

    Google Scholar 

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De-Shuang Huang Laurent Heutte Marco Loog

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© 2007 Springer-Verlag Berlin Heidelberg

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Zhou, B., Wang, Hx., Wang, Cr. (2007). A Hierarchical Clustering Algorithm Based on GiST. In: Huang, DS., Heutte, L., Loog, M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Contemporary Intelligent Computing Techniques. ICIC 2007. Communications in Computer and Information Science, vol 2. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74282-1_15

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  • DOI: https://doi.org/10.1007/978-3-540-74282-1_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-74281-4

  • Online ISBN: 978-3-540-74282-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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