ITM Web Conf.
Volume 11, 20172017 International Conference on Information Science and Technology (IST 2017)
|Number of page(s)||8|
|Section||Session IV: High Performance Computation|
|Published online||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
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.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.