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      • Computing and Mathematical Sciences
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      Clustering large datasets using cobweb and K-means in tandem

      Li, Mi; Holmes, Geoffrey; Pfahringer, Bernhard
      DOI
       10.1007/978-3-540-30549-1_33
      Link
       www.springerlink.com
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      Citation
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      Li, M., Holmes, G. & Pfahringer, B. (2005). Clustering large datasets using cobweb and K-means in tandem. In G.I. Webb & Xinghuo Yu(Eds.), Proceedings of the 17th Australian Joint Conference on Artificial Intelligence, Cairns, Australia, December 4-6, 2004. (pp. 368-379). Berlin: Springer.
      Permanent Research Commons link: https://hdl.handle.net/10289/1461
      Abstract
      This paper presents a single scan algorithm for clustering large datasets based on a two phase process which combines two well known clustering methods. The Cobweb algorithm is modified to produce a balanced tree with subclusters at the leaves, and then K-means is applied to the resulting subclusters. The resulting method, Scalable Cobweb, is then compared to a single pass K-means algorithm and standard K-means. The evaluation looks at error as measured by the sum of squared error and vulnerability to the order in which data points are processed.
      Date
      2005
      Type
      Conference Contribution
      Publisher
      Springer, Berlin
      Collections
      • Computing and Mathematical Sciences Papers [1452]
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