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Abstract: We propose a new approach for using online competitive learning on binary data. The usual Euclidean distance is replaced by binary distance measures, which take possible asymmetries of binary data into account and therefore provide a "different point of view" for looking at the data. The method is demonstrated on two artificial examples and applied on tourist marketing research data. 1 Introduction Most common clustering methods such as k-means, (hard and soft) competitive learning or neural... (Update)
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...2. 2 Hard Competitive Learning with Binary Distance Measure Another modi cation of a standard cluster algorithm is proposed by Leisch et al. 1998). They take hard competitive learning as base algorithm and replace the usual symmetric distances such as Euclidean or absolute...
...then the correct cluster centers are the respective medians. Recently several extensions to non Euclidean distances have been proposed [5], 6] Popular partitioning algorithms include classic methods like the k means algorithm and its online variants (which are often...
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BibTeX entry: (Update)
Leisch, F., Weingessel, A., & Dimitriadou, E. (1998). Competitive learning for binary valued data. In Niklasson, L., Boden, M., & Ziemke, T. (eds.), Proceedings of the 8th International Conference on Articial Neural Networks (ICANN 98), vol. 2, pp. 779-784, Skovde, Sweden. http://citeseer.comp.nus.edu.sg/24194.html More
@misc{ leisch98competitive,
author = "F. Leisch and A. Weingessel and E. Dimitriadou",
title = "Competitive learning for binary valued data",
text = "Leisch, F., Weingessel, A., & Dimitriadou, E. (1998). Competitive learning
for binary valued data. In Niklasson, L., Boden, M., & Ziemke, T. (eds.),
Proceedings of the 8th International Conference on Articial Neural Networks
(ICANN 98), vol. 2, pp. 779-784, Skovde, Sweden.",
year = "1998",
url = "citeseer.comp.nus.edu.sg/24194.html" }
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