基于模糊c均值算法的犯罪分析与预测

B. Sivanagaleela, S. Rajesh
{"title":"基于模糊c均值算法的犯罪分析与预测","authors":"B. Sivanagaleela, S. Rajesh","doi":"10.1109/ICOEI.2019.8862691","DOIUrl":null,"url":null,"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.","PeriodicalId":212501,"journal":{"name":"2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"24","resultStr":"{\"title\":\"Crime Analysis and Prediction Using Fuzzy C-Means Algorithm\",\"authors\":\"B. Sivanagaleela, S. Rajesh\",\"doi\":\"10.1109/ICOEI.2019.8862691\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":212501,\"journal\":{\"name\":\"2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI)\",\"volume\":\"36 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-04-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"24\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICOEI.2019.8862691\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICOEI.2019.8862691","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 24

摘要

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。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Crime Analysis and Prediction Using Fuzzy C-Means Algorithm
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.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Artery and Vein classification for hypertensive retinopathy Biometric Personal Iris Recognition from an Image at Long Distance Iris Recognition Using Visible Wavelength Light Source and Near Infrared Light Source Image Database: A Short Survey□ Brain Computer Interface Based Smart Environment Control IoT Based Smart Gas Management System
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1