Boosted Constrained K-Means Algorithm for Social Networks Circles Analysis

The volume of information generated by a huge number of social networks users is increasing every day. Social networks analysis has gained intensive attention in the data mining research community to identify circles of users depending on the characteristics in the individual profiles or the structure of the network. In this paper, we propose the boosting principle to find the circles of social networks. Constrained k-means clustering method is used as a weak learner with the boosting framework. This method generates a constrained clustering represented by a kernel matrix according to the priorities of the pair-wise constraints. The experimental results show that the proposed algorithm using boosting principle for social network analysis improves the performance of the clustering and outperforms the state-of-the-art.

@article{Iswed2019,
  title     = {Boosted Constrained K-Means Algorithm for Social Networks Circles Analysis},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {10},
  number    = {7},
  year      = {2019},
  publisher = {The Science and Information Organization},
  author    = {Intisar M Iswed and Yasser F. Hassan and Ashraf S. Elsayed},
  doi       = {10.14569/IJACSA.2019.0100758},
  url       = {https://doi.org/10.14569/IJACSA.2019.0100758}
}