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
Discovering Movie Categories Based on SPHK-means Clustering Algorithm
Wuyi University, School of Computer, 529020 Jiangmen, China
a Corresponding author: email@example.com
Basing on SPHK-means, an improved K-means clustering algorithm, we have used dataset provided by MovieLens to design experiment. First, we have reduced dimensions of movie-user scoring matrix. Then, we have multiply sampled movies to conduct agglomerative hierarchical clustering in order to determine the appropriate value of k and initial centers. Finally, according to fixed k and initial centers, we have divided movies into groups through K-means clustering. With evaluation indicators as precision, recall and number of groups found, experiment in this paper has indicated that result of SPHK-means clustering algorithm is better than that of classical K-means clustering algorithm.
© Owned by the authors, published by EDP Sciences, 2017
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