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- W2375800576 abstract "A parallel version k-means clustering algorithm,which can make clustering time decrease linearly as the number of host nodes increase,is proposed to meet the requirements of efficient process of large clustering data sets.In order to balance the detection rate and false positive rate better,a re-positioning algorithm is also presented based on the minimum squared error.The re-positioning algorithm can make the detection rate improved by 5%,and the false positive rate decreased by 1.1% compared with Li Na's algorithm.The experimental results show that the proposed algorithm in this paper can not only improve clustering efficiency,but also detect both known and unknown attacks more effectively." @default.
- W2375800576 created "2016-06-24" @default.
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- W2375800576 date "2012-01-01" @default.
- W2375800576 modified "2023-09-23" @default.
- W2375800576 title "Unsupervised Anomaly Detection Research Based on Parallel Clustering Algorithm" @default.
- W2375800576 hasPublicationYear "2012" @default.
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