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Computer Science ››2015,Vol. 42 ››Issue (12): 247-250.

Novel Global Kmeans Clustering Algorithm for Big Data

LI Bin, WANG Jin-song and HUANG Wei

Online:2018-11-14Published:2018-11-14

Abstract:The clustering method for big data has attracted lots of interest in recent years.This paper proposed a novel global k-means clustering algorithm (NGKCA).The proposed clustering method comprises four phrases,namely row dimension reduction phrase,line dimension reduction phrase,global k-means clustering phrase and the adjustment of clustering center point.The row dimension reduction phrase is realized by means of spectral clustering method,while the line dimension reduction phrase is realized with the aid of particle swarm optimization.Both the row dimension reduction phrase and the line dimension reduction phrase are completed,and then the global k-means clustering phrase and the PSO phrase proceed.The experiments were carried out on some well-known machine learning data set and a standard network security data set KDDCUP99.Experimental results show that the proposed NGKCA leads to superior perfor-mance in comparison with some common algorithms reported in the literature and the time complexity of the NGKCA is better than the algorithm of global k-means.

Key words:Global Kmeans,Spectral clustering,PSO,Clustering,KDDCUP99

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LI Bin, WANG Jin-song and HUANG Wei. Novel Global Kmeans Clustering Algorithm for Big Data[J].Computer Science, 2015, 42(12): 247-250.

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