Computer Science ›› 2016, Vol. 43 ›› Issue (12): 218-222.doi: 10.11896/j.issn.1002-137X.2016.12.040

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Random Forests Based Method for Inferring Social Ties of LBS Users

MA Chun-lai, SHAN Hong, MA Tao and GU Zheng-hai   

  • Online:2018-12-01 Published:2018-12-01

Abstract: Inferring social ties from the location information of LBS users,which can provide more information for group discovery and community detection,is now becoming a new problem in intelligence mining from location big data.Based on the theory of co-occurrences,the features of co-occurrences region were divided into four categories,and a new me-thod based on random forests for social ties inferring was proposed in this paper.The method consists of feature selection phase and classification phase.Firstly,for the problem that uncorrelatedand redundant features will affect the accuracy of result,an algorithm based on Fisher criterion and χ2 test was proposed to remove the uncorrelated and redundant features.Secondly,random forests was applied in the classification to overcome the problem of existing method that training phase is slow and the model is easily over-fitting.Check-in data of LBSN users is chosen as test data in experiment,the results indicate the feasibility and effectiveness of the method.

Key words: LBS,Spatio-temporal co-occurrences,Random forests,Social ties inferring

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