Industrial IoT Technologies and Applications. Second EAI International Conference, Industrial IoT 2017, Wuhu, China, March 25–26, 2017, Proceedings

Research Article

An Optimized Clustering Method with Improved Cluster Center for Social Network Based on Gravitational Search Algorithm

  • @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
Liping Sun1, Tao Tao1, Fulong Chen1, Yonglong Luo1,*
  • 1: Anhui Normal University
*Contact email: ylluo@ustc.edu.cn

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.