Abstract
We present a non-hierarchal clustering algorithm that can determine the optimal number of clusters by using iterations of k-means and a stopping rule based on BIC. The procedure requires twice the computation of k-means. However, with no prior information about the number of clusters, our method is able to get the optimal clusters based on information theory instead of on a heuristic method.
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References
Huang, Zhexue: Extension to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values, Data Mining and Knowledge Discovery, 2[3] (1998) 283–304.
Hardy, Andre: On the Number of Clusters, Computational Statistics & Data Analysis, l[23] (1996) 83–96.
Hartigan, J.A. and Wong, M.A.: A K-means clustering algorithm. Applied Statistics 28 (1979) 100–108.
MacQueen, J.B.: “Some methods for Classi_cation and Analysis of Multivariate Observations,” Proc. Symp. Math. Statist. and Probability, 5th Berkeley, 1 (1967) 281–297.
Pelleg, Dan and Andrew Moore: X-means: Extending K-means with Efficient Estimation of the Number of Clusters, ICML-2000 (2000).
Pelleg, Dan and Andrew Moore: Accelerating Exact k-means Algorithms with Geometric Reasoning, KDD-99 (1999).
Schwarz, G.: Estimating the dimension of a model, Ann. Statist., 6-2: (1978) 461–464.
Vesanto, Juha and Johan Himberg and Esa Alhoniemi and Juha Parhankangas: Self-Organizing Map in Matlab: the SOM Toolbox, Proceedings of the Matlab DSP Conference 1999, Espoo, Finland, November (1999) 35–40.
Yang, Ming-Hsuan and Narenda Ahuja: A Data Partition Method for Parallel Self-Organizing Map, Proceeding of the 1999 IEEE International Joint Conference on Neural Networks (IJCNN 99), Washington DC, July (1999).
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Ishioka, T. (2000). Extended K-means with an Efficient Estimation of the Number of Clusters. In: Leung, K.S., Chan, LW., Meng, H. (eds) Intelligent Data Engineering and Automated Learning — IDEAL 2000. Data Mining, Financial Engineering, and Intelligent Agents. IDEAL 2000. Lecture Notes in Computer Science, vol 1983. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44491-2_3
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DOI: https://doi.org/10.1007/3-540-44491-2_3
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