The selection of burglary cases based on multidimensional features and PageRank. (30th November 2021)

• Record Type: Journal Article
• Authors: Zhong, Han; Li, Zheng; Chen, Peng; Lu, Hao; Xu, Yijia
• Is Part Of: Concurrency and computation. Volume 34:Number 10(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-11-30
• Subjects: burglary cases -- multidimensional features -- PageRank -- sorting; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.6723
• Languages: English
• ISSNs: 1532-0626

Abstract

Abstract: With the rapid development of urbanization, a series of burglary cases occurred frequently. It is very important to push suspicious cases according to the top rank in order to improve the ability of intelligent concatenation cases. This article proposes a method of selecting burglary cases based on multidimensional features and PageRank, which could analyze the fact description of burglary cases, extract the multidimensional features of cases and perform feature representation to obtain the feature vector of each case. Then it constructed the network through the similarity between the multidimensional features and used PageRank to calculate the importance of each case for ranking. At last, the algorithm is verified based on the real data of burglary cases. The experimental results showed that the robustness test is better than the other two methods. And the proposed method is more effective and can be better applied to the sorting of burglary cases.