Research Article
An Optimized Clustering Method with Improved Cluster Center for Social Network Based on Gravitational Search Algorithm
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@INPROCEEDINGS{10.1007/978-3-319-60753-5_7, author={Liping Sun and Tao Tao and Fulong Chen and Yonglong Luo}, title={An Optimized Clustering Method with Improved Cluster Center for Social Network Based on Gravitational Search Algorithm}, proceedings={Industrial IoT Technologies and Applications. Second EAI International Conference, Industrial IoT 2017, Wuhu, China, March 25--26, 2017, Proceedings}, proceedings_a={INDUSTRIALIOT}, year={2017}, month={9}, keywords={Data clustering Gravitational Search Algorithm Density peaks clustering Social network}, doi={10.1007/978-3-319-60753-5_7} }- Liping Sun
Tao Tao
Fulong Chen
Yonglong Luo
Year: 2017
An Optimized Clustering Method with Improved Cluster Center for Social Network Based on Gravitational Search Algorithm
INDUSTRIALIOT
Springer
DOI: 10.1007/978-3-319-60753-5_7
Abstract
Data clustering is a kind of data analysis techniques for grouping the set of data objects into clusters. To make use of the advantages of distance measure and nearest neighbor method, we present a hybrid data clustering algorithm based on GSA and DPC (GSA-DPC) algorithm. The optional clustering center set is selected by DPC algorithm. In turn, we optimize the clustering center set to achieve the best clustering distribution under the fame of GSA. Its performance is compared with four related clustering algorithms. The simulation results demonstrate the effectiveness of the presented algorithm.
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