A Parallel Clustering Algorithm for Power Big Data Analysis

With the fast development of information technology, the power data is growing at an exponentially speed. In the face of multi-dimensional and complicated power network data, the performance of the traditional clustering algorithms are not satisfied. How to effectively cope with the power network data is becoming a hot topic. This paper proposes a parallel implement of K-means clustering algorithm based on Hadoop distributed file system and Mapreduce distributed computing framework to deal this problem. The experimental results show that the performance of our proposed algorithm significantly outperforms the traditional clustering algorithm and the parallel clustering algorithm can significantly reduce the time complexity and can be applied in analyzing and mining of the power network data.