A New Improved Hybridized K-MEANS Clustering Algorithm with Improved PCA Optimized with PSO for High Dimensional Data Set

Provided by: International Journal of Soft Computing and Engineering (IJSCE)
Topic: Data Management
Format: PDF
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 optimization.

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