Abstract
We proposed the methodology of introducing topographic component to conventional clustering measures for the evaluation of the SOM using external criteria, i.e., class information. The topographic measure evaluates clustering accuracy together with topographic connectivity of class distribution on the topology space of the SOM. The topographic component is introduced by marginalization of basic statistics to the set-based measures, and by a likelihood function to the pairwise-based measures. Our method can extend any clustering measure based on set or pairwise of data points. The present paper examined the topographic component of the extended measure and revealed an appropriate neighborhood radius of the topographic measures.
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References
Fukui, K., Saito, K., Kimura, M., Numao, M.: Sequence-based som: Visualizing transition of dynamic clusters. In: Proc. of IEEE 8th International Conference on Computer and Information Technology (CIT 2008), pp. 47–52 (2008)
Fukui, K., Sato, K., Mizusaki, J., Numao, M.: Kullback-leibler divergence based kernel SOM for visualization of damage process on fuel cells. In: Proc. of 22nd IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2010)., vol. 1, pp. 233–240 (2010)
Goodhill, G.J., Sejnowski, T.J.: Quantifying neighbourhood preservation in topographic mappings. In: Proc. of the 3rd Joint Symposium on Neural Computation, vol. 6, pp. 61–82 (1996)
Kiviluoto, K.: Topology preservation in self-organizing maps. In: Proc. of International Conference on Neural Networks (ICNN 1996), pp. 294–299 (1996)
Kohonen, T.: Self-Organizing Maps. Springer, Heidelberg (1995)
Koskela, M., Laaksonen, J., Oja, E.: Entropy-based measures for clustering and SOM topology preservation applied to content-based image indexing and retrieval. In: Proceedings of the 17th International Conference on Pattern Recognition (ICPR 2004), vol. 2, pp. 1005–1009 (2004)
Lagus, K., Kaski, S., Kohonen, T.: Mining massive document collections by the websom method. Information Sciences 163, 135–156 (2004)
Oja, M., Kaski, S., Kohonen, T.: Bibliography of self-organizing map (som) papers: 1998-2001 addendum. Neural Computing Surveys 3, 1–156 (2002)
Uriarte, E.A., Martín, F.D.: Topology preservation in SOM. International Journal of Mathematical and Computer Sciences 1(1), 19–22 (2005)
Veenhuis, C., Koppen, M.: Data Swarm Clustering, ch. 10, pp. 221–241. Springer, Heidelberg (2006)
Xu, R., Wunsch, D.: Cluster Validity. Computational Intelligence, ch. 10, pp. 263–278. IEEE Press, Los Alamitos (2008)
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Fukui, Ki., Numao, M. (2011). Topographic Measure Based on External Criteria for Self-Organizing Map. In: Laaksonen, J., Honkela, T. (eds) Advances in Self-Organizing Maps. WSOM 2011. Lecture Notes in Computer Science, vol 6731. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21566-7_13
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DOI: https://doi.org/10.1007/978-3-642-21566-7_13
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