ITM Web Conf.
Volume 11, 20172017 International Conference on Information Science and Technology (IST 2017)
|Number of page(s)
|Session IV: High Performance Computation
|23 May 2017
An Improved K-means Clustering Algorithm Applicable to Massive High-dimensional Matrix Datasets
Wuyi University, School of Computer, 529020 Jiangmen, China
a Corresponding author: firstname.lastname@example.org
Since K-means clustering algorithm is easy to implement and high efficient, it has been widely used in cluster analysis of massive datasets. The value of k is difficult to determine in advance and the randomness of choosing initial centers leads to a series of social problems, such as instability, local optimal solution sensitivity to outliers. Results from hierarchical clustering are more natural than those from K-means clustering, but its high time complexity and space complexity makes it difficult to be applied to a large data set. In this paper, through combination of hierarchical clustering and K-means clustering, we have proposed an improved K-means clustering algorithm, and have done experiments using datasets provided by MovieLens.
© Owned by the authors, published by EDP Sciences, 2017
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