Time, Place, and Modus Operandi - A Simple Apriori Algorithm Experiment for Crime Pattern Detection.
IISA(2018)
摘要
Given the fast-paced nature of modern police work, the development and use of advanced data mining tools for crime analysis can play a critical factor in mitigating future harm and helping with crime prevention. This paper aims to solve the problem of identifying potential serial offending patterns using previously underutilised attributes from police-recorded crime data. To achieve this a crime data processing procedure is proposed that extracts three variables in police-recorded crime event data: (1) time; (2) setting; and (3) modus operandi. Each crime-event attribute is modeled using the Apriori algorithm, commonly used for frequent item set mining and association rule learning from complex datasets. Results from the model suggest that Apriori can identify significant associations and thus can highlight crime pattern trends nested within broader police-recorded crime databases, which could lead to more effective police responses than currently offered via traditional analytical methods.
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关键词
Law enforcement,Bicycles,Data mining,Tools,Analytical models,Classification algorithms,Data models
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