基于聚类的数据加权优化在犯罪预测中的应用

近年来,我国传统暴力犯罪与成年人犯罪呈下降态势,但是,犯罪案由层出不穷。为有效提升公安实践工作中犯罪预测能力,打击各类违法犯罪事件,本文针对犯罪数据,提出一种新型犯罪预测模型。利用密度聚类分析方法将犯罪数据分类,然后进行数据降维提取关键属性生成特征数据,继而对特征数据进行加权优化并采用机器学习的方式对特征数据进行学习,从而预测犯罪案由。实验结果表明,与传统方法相比,本文方法具有更好的预测效果,为公安实践工作中类似案件的侦破和预防,提供新的路径支撑。

Application of Data Weighting Optimization Based on Clustering in Crime Prediction

In recent years, traditional violent crimes and adult crimes in China have shown a downward trend. However, the types of crimes are endless. In order to effectively improve the ability of crime prediction in the public security practice and combat various types of illegal and criminal events, a new crime prediction model is proposed for crime data in this paper. The crime data are classified by density clustering analysis method, then the data is dimension-reduced to extract key attributes to generate feature data, and then the feature data are weighted and optimized, and the data are learned by the way of machine learning to predict the type of crime. The experimental results show that compared with the traditional methods, the proposed method has better prediction effect, providing a new path support for the detection and prevention of similar cases in the public security practice.