ITM Web Conf.
Volume 11, 20172017 International Conference on Information Science and Technology (IST 2017)
|Number of page(s)||7|
|Section||Session IV: High Performance Computation|
|Published online||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
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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