| Title: |
Distributed Data Mining |
| Author(s): |
G. Tsoumakas, I. Vlahavas.
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| Availability: |
Click here to download the PDF (Acrobat Reader) file (16 pages).
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| Keywords: |
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| Appeared in: |
Encyclopedia of Data Warehousing and Mining - 2nd Edition, John Wang (Ed.), Idea Group Reference, pp. 709-715, 2008.
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| Abstract: |
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| See also : |
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This paper has been cited by the following:
| 1 |
Uyen, N.T.V., Lee S.G., Chung, T.C. “A new framework for distributed boosting algorithm”, Proc. 2007 Int. Conf. on Future Generation Communication and Networking (FGCN 2007), pp. 420-423, 2007.
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| 2 |
Uyen, N.T.V., Lee S.G., Chung, T.C. “A New Boosting Algorithm for Classification on Distributed Databases”, International Journal of Softfware Engineering and Its Applications 2 (2), April, 2008.
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| 3 |
Peteiro-Barral, D., Guijarro-Berdiñas, B., Pérez-Sánchez, B. (2011) On the effectiveness of distributed learning on different class-probability distributions of data, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7023 LNAI, pp. 114-123.
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| 4 |
Stanković, S.V., Rakočevic, G., Kojić, N., Milićev, D. (2012) A classification and comparison of Data Mining algorithms for Wireless Sensor Networks, 2012 IEEE International Conference on Industrial Technology, ICIT 2012, Proceedings, art. no. 6209949, pp. 265-270. |
| 5 |
Bhamra, G.S., Verma, A.K., Patel, R.B. (2012) Agent enriched distributed association rules mining: A review, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7103 LNAI, pp. 30-45.
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