Crime-related Factor Analysis Based On Data Mining Technology

Public Security systems constantly promote the information construction in the many years of practical work. At the same time, Public Security Bureau has been the very large of the data and information through the accumulation of long-term work. There is an important and meaningful issue for public security system that using data mining technology to solve the problem of the crime-related factor analysis. Compared with traditional data analysis techniques, data mining can find knowledge from the existing data model and extract the data into knowledge.In order to find the factors affecting crime, classification methods, clustering methods and Bayesian network methods was introduced to excavate knowledge from criminals of background information, psychological information and genetic information. The main research work in the paper included the following aspects:1) We analyzed the data set of criminals by the classification method and clustering methods. We selected the decision tree ID3classifier, the C4.5decision tree classifier and the Naive Bayes classifier in classification method. K-means partition clustering and BIRCH hierarchical clustering were Selected in the clustering method.2) The traditional K2algorithm with a variable order to limit the search space, the K2algorithm used a random mode to generate a variable sequence. So this paper presented an improved Bayesian network structure learning K2-P algorithm. New algorithm based on conditional independence SGS and PC2algorithm improved the Bayesian network structure learning and generated the topology map which contains the data knowledge. The topology sequence sets were generated through the full topology filter as the variable order in the next step of structure learning. The experimental results showed that K2-P algorithm could get a Bayesian network which owned a higher Bayesian Dirichlet score than the traditional K2algorithm. 3) The search of Bayesian network structure is an NP-Hard problem. When the traditional K2algorithm searched the parent node sets for each attribute node, the greedy strategy was used to search structure. A simple greedy strategy might give up a better solution, so we designed the K2-EX algorithm in this paper. The new algorithm could get better the score of Bayesian Dirichlet by jumping search, further, we defined an fitness function to control jumping times. The experimental results proved that the K2-EX algorithm could achieve better network structure on different data sets.4) Finally, we carried out criminal factor analysis and found significantly associated attributes through improved Bayesian network algorithm. For example the DRD4gene with the types of crime, psychological factors and age of the offender. We draw some meaningful conclusions for the public security system.