Search

Graphs have been widely applied in many real applications like knowledge mining of social networks, pattern recognition, biological analysis, etc. However, precise matching for large graphs has been proven to be a NP-hard problem. In the context, an efficient similarity join method is proposed to quickly seek similar candidate graph pairs from a large number of graphs in the paper. It is a new method, where local sensitive hash (LSH) and Minhash are used to sharply reduce the time needed to compare candidate graph pairs as well as improve the quality of similarity matching through graph associated vertex degree matrix. Furthermore, the bound filtering strategy is adopted to avoid invalid comparing between graph pairs, which can improve graph similarity join efficiency. To test the advantages of our method, various experiments on synthetic data and real data were conducted. The results of these experiments illustrate that the method in this paper has both good efficiency and effectiveness. In terms of the running time and the precision of the algorithm, the new method yields better results than the other similar methods.

Aiming to aid Electric-Power Industry to accurately understand users, hybrid learning model based ensemble learning algorithms for recognizing user to be sensitive to electric charge is proposed in this paper. On the basis of big data presented by CCF competition sponsor in China, with some excellent technology or algorithm such as JieBa, SFFS, etc., we extract many key features from data set and successfully draw a portrait for users who pay close attention to electric charge. Furthermore, machine learning algorithms and the strategy selection model related to them are investigated. The feasibility that hybrid learning model combining several ensemble learning algorithms can substantially improve classification accuracy are proved from theoretical level. Then the details of implementing hybrid learning model are described in the paper. Lastly, the hybrid learning model named Stacking is achieved, which yields better performance in contrast to the state-of-the-art competitors. The experimental results indicate that Stacking has both high precision and recall with 0.8 and 0.85 respectively. Furthermore the F1 score of Stacking evaluation is 0.823.