Abstract
To date, not many studies have been conducted on criminal prediction. In this study, the criminal data related to city S is divided into a training data set and a validation data set at a 1:1 ratio in light of the personal tag data and the travel and accommodation data of criminals and ordinary people in city S. Firstly, the FP-growth algorithm is adopted to calculate association rules between the criminals and the ordinary people in their travel and hotel accommodation data, in order to discover criminal suspects based on association rules. Secondly, the DBSCAN algorithm is employed for clustering of the tag data of the criminals and the ordinary people, followed by similarity calculation, in order to discover criminal suspects based on tag clustering. Lastly, intersection operation is performed on the above two sets of criminal suspects, and the resulting intersection is verified against the criminal validation set for elimination of criminals who appear in the intersection so as to obtain final criminal suspects. Results show that a set of 648 criminal suspects is retrieved based on the association rules calculated by the FP-growth algorithm, while a set of 973 criminal suspects is retrieved based on DBSCAN clustering and cosine similarity of the personal tags; the number of criminal suspects is narrowed down to 567 after the intersection operation of the two sets, and 419 of the 567 criminal suspects are further verified to be criminals using the validation set, thereby leaving the other 148 to be the final criminal suspects and giving a prediction accuracy of 73.9%. The data mining method of criminal suspects based on association rules and tag clustering in this study has been successfully applied to the police system of city S, and the experiment proves the effectiveness of this method in detecting criminal suspects.
Highlights
Nowadays, crime situation is becoming increasingly serious across the globe with more crime types and a higher number of criminals, posing a threat to human lives and property as well as social stability
The DBSCAN algorithm is employed for clustering of the tag data of the criminals and the ordinary people, followed by similarity calculation, in order to discover criminal suspects based on tag clustering
Results show that a set of 648 criminal suspects is retrieved based on the association rules calculated by the FP-growth algorithm, while a set of 973 criminal suspects is retrieved based on DBSCAN clustering and cosine similarity of the personal tags; the number of criminal suspects is narrowed down to 567 after the intersection operation of the two sets, and 419 of the 567 criminal suspects are further verified to be criminals using the validation set, thereby leaving the other 148 to be the final criminal suspects and giving a prediction accuracy of 73.9%
Summary
Crime situation is becoming increasingly serious across the globe with more crime types and a higher number of criminals, posing a threat to human lives and property as well as social stability. Given the constantly generated crime data, it is necessary for data analysts to reveal hidden patterns in the data, analyze implicit relationships between the data, predict occurrence of crimes and discover potential criminals, so as to improve the efficiency of law enforcement efficiency of public security authorities and prevent occurrence of crimes. It is possible to extract relevant criminal evidence from association rules of a large number of data items, and further explore the patterns, trends and links between different crimes, so as to provide support for the police in case investigation and crime prevention. In the international research community of association-rule-based crime mining, Ng et al [2] introduced temporal association rules and proposed an incremental algorithm to solve the problem of how to process time series whose association rules contain time expressions, and employed the new algorithm to discover crime patterns in Hong Kong. Based on geographic and demographic factors, Asmai et al [10] used ARM to generate a crime mapping model for crime analysis, employing the model to examine the occurrence of crime at a specific location, and demonstrating that the model could be used to analyze future crime locations with
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