Frequent pattern mining(FPM)is one of the most important problems in graph mining.The FPM problem is defined as mining all the patterns,with frequency above a user-defined threshold in a large graph.In recent years,with the popularity of social networks and so on,single-graph-based FPM has received more and more attention.Investigators have developed considera-ble techniques,while most of them suffer from high computational cost,inconvenient result inspection and inconvenient in parallel computation.To tackle the issues,this paper proposes an approach to discover diversified top-k patterns from singe large graphs.This paper first designs a diversification function to measure the diversity of patterns,then develops a distributed algorithm with early termination property named DisTopk,to efficiently identify diversified top-k patterns,from distributive stored graphs.Expe-rimental results conducted on real-life and synthetic graphs show that DisTopk can mine diversified top-k patterns more efficient-ly than traditional algorithms.
万方数据
维普
