Abstract
This paper reports design of a pattern classifying machine (PCM) for distributed data mining (DDM) environment. The proposed PCM is based on the computing model of a special class of sparse network referred to as Cellular Automata (CA). Extensive experimental results confirm scalability of the PCM to handle distributed datasets. The excellent classification accuracy and low memory overhead figure establish the proposed PCM as the classifier ideally suited for DDM environments.
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Maji, P., Sikdar, B.K., Chaudhuri, P.P. (2004). Cellular Automata Evolution for Distributed Data Mining. In: Sloot, P.M.A., Chopard, B., Hoekstra, A.G. (eds) Cellular Automata. ACRI 2004. Lecture Notes in Computer Science, vol 3305. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30479-1_5
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DOI: https://doi.org/10.1007/978-3-540-30479-1_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23596-5
Online ISBN: 978-3-540-30479-1
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