基于心理和犯罪学特征的青少年罪犯聚类研究

M. Breitenbach, T. Brennan, W. Dieterich, G. Grudic
{"title":"基于心理和犯罪学特征的青少年罪犯聚类研究","authors":"M. Breitenbach, T. Brennan, W. Dieterich, G. Grudic","doi":"10.3233/978-1-60750-633-1-123","DOIUrl":null,"url":null,"abstract":"In Criminology research the question arises if certain types of delinquents can be identified from data, and while there are many cases that can not be clearly labeled, overlapping taxonomies have been proposed in [1,2,3]. In a recent study Juvenile offenders (N = 1572) from three state systems were assessed on a battery of criminogenic risk and needs factors and their official criminal histories. Cluster analysis methods were applied. One problem we encountered is the large number of hybrid cases that have to belong to two or more classes. To eliminate these cases we propose a method that combines the results of Bagged K-Means and the consistency method [4], a semi-supervised learning technique. A manual interpretation of the results showed very interpretable patterns that were linked to existing criminologic research.","PeriodicalId":438467,"journal":{"name":"Data Mining for Business Applications","volume":"29 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Clustering of Adolescent Criminal Offenders using Psychological and Criminological Profiles\",\"authors\":\"M. Breitenbach, T. Brennan, W. Dieterich, G. Grudic\",\"doi\":\"10.3233/978-1-60750-633-1-123\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In Criminology research the question arises if certain types of delinquents can be identified from data, and while there are many cases that can not be clearly labeled, overlapping taxonomies have been proposed in [1,2,3]. In a recent study Juvenile offenders (N = 1572) from three state systems were assessed on a battery of criminogenic risk and needs factors and their official criminal histories. Cluster analysis methods were applied. One problem we encountered is the large number of hybrid cases that have to belong to two or more classes. To eliminate these cases we propose a method that combines the results of Bagged K-Means and the consistency method [4], a semi-supervised learning technique. A manual interpretation of the results showed very interpretable patterns that were linked to existing criminologic research.\",\"PeriodicalId\":438467,\"journal\":{\"name\":\"Data Mining for Business Applications\",\"volume\":\"29 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-08-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Data Mining for Business Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3233/978-1-60750-633-1-123\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Data Mining for Business Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3233/978-1-60750-633-1-123","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

在犯罪学研究中,如果可以从数据中识别出某些类型的违法者,那么问题就出现了,尽管有许多案例无法明确标记,但在[1,2,3]中提出了重叠的分类。在最近的一项研究中,对来自三个州的青少年罪犯(N = 1572)进行了一系列犯罪风险和需求因素以及他们的正式犯罪历史的评估。采用聚类分析方法。我们遇到的一个问题是大量的混合案例必须属于两个或更多的类。为了消除这些情况,我们提出了一种结合Bagged K-Means结果和一致性方法[4]的方法,这是一种半监督学习技术。对结果的人工解释显示了与现有犯罪学研究相关的非常可解释的模式。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Clustering of Adolescent Criminal Offenders using Psychological and Criminological Profiles
In Criminology research the question arises if certain types of delinquents can be identified from data, and while there are many cases that can not be clearly labeled, overlapping taxonomies have been proposed in [1,2,3]. In a recent study Juvenile offenders (N = 1572) from three state systems were assessed on a battery of criminogenic risk and needs factors and their official criminal histories. Cluster analysis methods were applied. One problem we encountered is the large number of hybrid cases that have to belong to two or more classes. To eliminate these cases we propose a method that combines the results of Bagged K-Means and the consistency method [4], a semi-supervised learning technique. A manual interpretation of the results showed very interpretable patterns that were linked to existing criminologic research.
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
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信