文章基本信息

标题：A New Improved Hybridized K-MEANS Clustering Algorithm with Improved PCA Optimized with PSO for High Dimensional Data Set
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作者：H.S. Behera ; Abhishek Ghosh ; Sipak Ku.Mishra 等
期刊名称：International Journal of Soft Computing & Engineering
电子版ISSN：2231-2307
出版年度：2012
卷号：2
期号：2
页码：121-126
出版社：International Journal of Soft Computing & Engineering
摘要：The day to day computation has made the data sets and data objects to grow large so it has become important to cluster the data in order to reduce complexity to some extent. K-means clustering algorithm is an efficient clustering algorithm to cluster the data, but the problem with the k-means is that when the dimension of the data set becomes larger the effectiveness of k-means is lost. PCA algorithm is used with k-means to counter the dimensionality problem. However K-means with PCA does not give much optimisation. It can be experimentally seen that the optimisation of k-means gives more accurate results. So in this paper we have proposed a PSO optimised k-means algorithm with improved PCA for clustering high dimensional data set.
关键词：Data mining; Clustering; Particle Component;Analysis; Centred vector; Squared Sum Error; Lower bound;Bound Error; Particle Swarm Optimisation.