Analysis and Experimentation of Grid-Based Data Mining with Dynamic Load Balancing

Authors:
Yong Beom Ma;Tae Young Kim;Seung Hyeon Song;Jong Sik Lee
Affiliations:
School of Information Engineering Inha University, Incheon, Republic of Korea 402-751;School of Information Engineering Inha University, Incheon, Republic of Korea 402-751;School of Information Engineering Inha University, Incheon, Republic of Korea 402-751;School of Information Engineering Inha University, Incheon, Republic of Korea 402-751
Venue:
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
Year:
2009

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Abstract

Algorithms and methods for analyzing large amounts of data are studied and developed. This paper presents a Data Mining (DM) method operated in grid computing environment. Because DM technology uses large amounts of data and requires costs to compute, utilizing and sharing computing data and resources are key issues in DM. Therefore, a Dynamic Load Balancing (DLB) algorithm and a decision range readjustment algorithm are proposed and applied to the Grid-based Data Mining (GDM) method. And we analyzed the average waiting time for learning and computing time. For a performance evaluation, the system execution time, computing time, and average waiting time for learning are measured. Experimental results show that GDM with the DLB method provides many advantages in terms of processing time and cost.