Improved K-MEANS Algorithm Based on Samples

Wei Jin; Xiao Rong Zhao

doi:10.4028/www.scientific.net/AMM.734.472

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 734Improved K-MEANS Algorithm Based on Samples

Improved K-MEANS Algorithm Based on Samples

Abstract:

Clustering analysis plays an important role in scientific research and commercial application. K-means algorithm is a widely used partition method in clustering. in this method.The number of clusters is predefined and the technique is highly dependent off the initial identification of elements that represent the clusters well. As the dataset’s scale increases rapidly, it is difficult to use K-means and deal with massive data. partitions.To prevent this problem,refining initial points algorithm provided.it can reduce execution time and improve solutions for large data by setting the refinement of initial conditions.The experiments demonstrate that sample-based K-means is more stable and more accurate.

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Periodical:

Applied Mechanics and Materials (Volume 734)

Pages:

472-475

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.734.472

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Online since:

February 2015

Authors:

Wei Jin*, Xiao Rong Zhao

Keywords:

Criterion Clustering, K-Means Algorithm, Squared-Error

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* - Corresponding Author

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