A novel clustering algorithm for large-scale graph processing

Abstract

The most important issue of big data processing is the relevance of analytical data; thought of this paper is to analyze the data as a graph optimal partitioning problem. Computing all circuit graphics firstly, calculated frequent map and redrawing of the system structure according to the results, the core problem is the time complexity of the algorithm. To solve this problem, researching DEMIX algorithm in non-strongly connected graph and study on relationship between frequent node and adjacency matrix which is strongly connected branches. Gives the corresponding examples, and analyzes the algorithm complexity. On the time complexity of the proposed method DEMIX is retrieving effect faster, more accurate search results.