Abstract
Using the basic idea of Super-paramagnetic Clustering (SPC), we propose a cellular automaton approach for data clustering: A data set is regarded as a Potts magnetic system and a short-range interaction is introduced between the neighboring spins. Let the system evolve automatically under the function of the spin-spin interaction and the thermal motion. Finally, at a proper temperature the system will reach the super-paramagnetic phase, in which the spins (data points) will form into a number of ‘magnetic domains’ (data clusters). We apply this method to some data sets with different structures and get satisfactory results.
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Botao, Z., Shuqiang, Z., Zhongqiu, Y. (2011). Cellular Automaton for Super-Paramagnetic Clustering of Data. In: Ma, M. (eds) Communication Systems and Information Technology. Lecture Notes in Electrical Engineering, vol 100. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21762-3_4
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DOI: https://doi.org/10.1007/978-3-642-21762-3_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21761-6
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