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.
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