A parallel fuzzy clustering algorithm for large graphs using Pregel

作者：

Highlights：

• We propose a parallel fuzzy clustering algorithm (PGFC) for large graphs.

• Pregel and Hadoop frameworks are used for processing of massive graph data.

• PGFC is scalable and produces good quality of clusters.

• The performance is validated using graphs having upto millions of nodes.

• Results reveal that PGFC significantly outperforms state-of-the-art algorithms.

摘要

•We propose a parallel fuzzy clustering algorithm (PGFC) for large graphs.•Pregel and Hadoop frameworks are used for processing of massive graph data.•PGFC is scalable and produces good quality of clusters.•The performance is validated using graphs having upto millions of nodes.•Results reveal that PGFC significantly outperforms state-of-the-art algorithms.

论文关键词：Clustering,Graphs,Big data mining,Fuzzy C-Mean,Pregel

论文评审过程：Received 12 December 2015, Revised 2 February 2017, Accepted 3 February 2017, Available online 8 February 2017, Version of Record 16 February 2017.

论文官网地址：https://doi.org/10.1016/j.eswa.2017.02.005