Abstract
Researchers in the field of data mining now confront a common problem that data mining tasks are time-consuming in that these tasks have to process large-scale datasets. Grid computing focuses on integrating distributed, heterogeneous and idle computers from the Internet to be a service system with high performance. Thus, it is possible to take advantage of grid computing to provide high performance computation capability to effectively reduce task durations. Here, we have successfully developed DMGrid, a grid handling data mining applications. In DMGrid, it not only considers efficient parallel computing as a crucial aspect, but also takes into account dynamic resource configuration. Unlike many existing data mining grids, DMGrid also provides an engine to execute the algorithm flow specified in an application. Moreover, it offers application execution monitoring. At last, we perform experiments and design two applications: Customer Churning Analysis and Customer Value Analysis through which the feasibility of DMGrid is validated.
This work is supported by the National Natural Science Foundation of China under Grant 60402011 and National Eleven Five-Year Scientific and Technical Support Plans under Grant 2006BAH03B05.
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Wang, Y., Xu, L., Geng, G., Zhao, X., Du, N. (2008). DMGrid: A Data Mining System Based on Grid Computing. In: Tang, C., Ling, C.X., Zhou, X., Cercone, N.J., Li, X. (eds) Advanced Data Mining and Applications. ADMA 2008. Lecture Notes in Computer Science(), vol 5139. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88192-6_29
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DOI: https://doi.org/10.1007/978-3-540-88192-6_29
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