Crime Analysis and Prediction Using Fuzzy C-Means Algorithm

B. Sivanagaleela, S. Rajesh
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引用次数: 24

Abstract

Crime analysis is methodological approach for identify the crime areas. The crime areas are mainly based on the crime type these identified crime areas are helpful to reduce the crime rate. This can be very easy to identify the crime areas, based on this process the crime rate can be analyzed. With the increasing of computer systems the crime data analysts can help to the crime investigators to analyze the crime. Based on the clustering and preprocessing extract the crime areas from a structured data. The cause of occurrences of crimes like crime details of person and other factors we are focusing mainly on crime factors of previous years. This system is mainly focus on in which area the crime will occur, does not focus on the identify the criminal. In the existing system naive bayes classification was used In the present system, the fuzzy C-Means algorithm will be use to cluster the crime data for total cognizable crimes such as Kidnapping, murder, Theft, Burglary, cheating, crime against women, robbery and other such crimes.
基于模糊c均值算法的犯罪分析与预测
犯罪分析是识别犯罪区域的方法论方法。犯罪区域主要是根据犯罪类型来确定的,这些犯罪区域的确定有助于降低犯罪率。这样就可以很容易地识别出犯罪区域,根据这个过程就可以对犯罪率进行分析。随着计算机系统的不断发展,犯罪数据分析人员可以帮助犯罪侦查人员对犯罪进行分析。在聚类和预处理的基础上,从结构化数据中提取犯罪区域。犯罪发生的原因,如犯罪细节和其他因素,我们主要关注的是前几年的犯罪因素。这一制度主要关注犯罪将在哪个地区发生,而不是关注罪犯的身份。在现有的系统中使用朴素贝叶斯分类,在本系统中,将使用模糊C-Means算法对绑架、谋杀、盗窃、入室盗窃、欺骗、侵害妇女、抢劫等全部可认知犯罪的犯罪数据进行聚类。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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