Analytical Modeling Of Data Mining Process Based On Distributed Tuple Space
Analytical modeling; Data mining; Parallel processing; Tuple space
The explosive growth in data volume imposes a big challenge to traditional data mining process in that data mining algorithms tend to be computationally intensive. Parallel and distributed data mining provides an attractive solution to the large scale data mining process. Message passing based parallel computing has been widely used in diverse applications such as scientific computing, ray tracing, simulation, and recently data mining. In this paper, we will present an alternative approach based on distributed tuple space that we contend provides more convenient and efficient parallel data mining framework. Then, we describe the associated analytical performance models to investigate the scalability of the proposed architecture over the cluster computing environment. We also present the experimental results for an exemplary data mining algorithm, k nearest neighbor.
Proceedings of the 2005 International Conference on Parallel and Distributed Processing Techniques and Applications, PDPTA'05
Number of Pages
Article; Proceedings Paper
Source API URL
Dai, Jianyong; Lee, Joohan; and Wang, Morgan C., "Analytical Modeling Of Data Mining Process Based On Distributed Tuple Space" (2005). Scopus Export 2000s. 3177.