Recent advances1 in computer networking and database technologies have resulted in creation of large quantities of data which are located in different sites. Data mining is a useful tool to extract valuable knowledge from this data. Well known data mining algorithms include association rule mining, classification, clustering, outlier detection, etc. However, extracting useful knowledge from distributed sites is often challenging due to real world constraints such as privacy, communication and computation overhead.
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Zhan, J., Matwin, S., Chang, L.W. (2008). Privacy-Preserving Naive Bayesian Classification over Horizontally Partitioned Data. In: Lin, T.Y., Xie, Y., Wasilewska, A., Liau, CJ. (eds) Data Mining: Foundations and Practice. Studies in Computational Intelligence, vol 118. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78488-3_31
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