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Cellular Automata Evolution for Distributed Data Mining

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Cellular Automata (ACRI 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3305))

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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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References

  1. Park, B., Kargupta, H.: Distributed Data Mining: Algorithms, Systems, and Applications. In: Ye, N. (ed.) To be published in the Data Mining Handbook (2002)

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  4. Ganguly, N., Maji, P., Dhar, S., Sikdar, B.K., Chaudhuri, P.P.: Evolving Cellular Automata as Pattern Classifier. In: Bandini, S., Chopard, B., Tomassini, M. (eds.) ACRI 2002. LNCS, vol. 2493, pp. 56–68. Springer, Heidelberg (2002)

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  5. Maji, P., Shaw, C., Ganguly, N., Sikdar, B.K., Chaudhuri, P.P.: Theory and Application of Cellular Automata For Pattern Classification. Fundamenta Informaticae (2003)

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© 2004 Springer-Verlag Berlin Heidelberg

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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

  • eBook Packages: Springer Book Archive

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