Zhongxue Yang
College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, 210016, Nanjing, China
Xiaolin Qin
College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, 210016, Nanjing, China
Wenrui Li
School of Mathematic and information Technology, Nanjing Xiaozhuang University, 211171, Nanjing, China
Yingjie Yang
Centre for Computational Intelligence, De Montfort University, Leicester, LE19BH, UK
ABSTRACT
A novel dynamic clustering algorithm for cloud computing is proposed in this study. Dynamic clustering algorithm originates from K-means algorithm, however, an important characteristic of dynamic clustering algorithm is dynamic in that the centroid of the new cluster is updated in each iteration and some certain points near the boundary may be labeled to a new cluster in next iteration. Cloud computing is a new generation of computation platform with the nature of widely distributed and heterogeneous environment. Hadoop as a cloud platform and Map/Reduce as a distributed computing architecture are described and K-means clustering algorithm is illustrated for comparison to evaluate the performance in this study as well. Following this, experiments on KDD DATA datasets are conducted and the result shows that Dynamic Clustering algorithm can exhibit an excellent performance with higher attack detection rate and lower false positive rate while comparison with K-means clustering algorithm.
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How to cite this article
Zhongxue Yang, Xiaolin Qin, Wenrui Li and Yingjie Yang, 2013. A Dynamic Clustering Algorithm for Cloud Computing. Information Technology Journal, 12: 4637-4641.
DOI: 10.3923/itj.2013.4637.4641
URL: https://scialert.net/abstract/?doi=itj.2013.4637.4641
DOI: 10.3923/itj.2013.4637.4641
URL: https://scialert.net/abstract/?doi=itj.2013.4637.4641
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