The aim of this article is to inquire about correlations between criminal phenomena and demographic factors. This international-level comparative study used a dataset covering 56 countries and 28 attributes. The data were processed with the Self-Organizing Map (SOM), assisted other clustering methods, and several statistical methods for obtaining comparable results. The article is an exploratory application of the SOM in mapping criminal phenomena through processing of multivariate data. We found out that SOM was able to group efficiently the present data and characterize these different groups. Other machine learning methods were applied to ensure groups computed with SOM. The correlations obtained between attributes were chiefly weak.
Data mining has in recent decades been an approach to research of approximately every major discipline. Law, in the sense of a scientific field dealing with the topics related to branches of laws, is increasingly in quest of facilitation from data mining as well. Crime, as one of the most attractive research fields, requires processing of data on wide-ranging factors, including demographic, socio-economic, and historical indicators. Data mining, clustering and visualizing techniques, have broadly shown their practical value in a variety of domains, and can be considered to play an essential role in the study of crime. The self-organizing map, which employs an unsupervised learning approach to cluster and visualize data in accordance with patterns identified in a dataset, is a competent instrument meant for such data exploration. The interconnection between artificial intelligence and the study of crime makes an innovative study achievable. In the past, in identifying abnormality of certain acts, the SOM has found its application in a broad range, for example, in the detection of automobile bodily injury insurance fraud (Brockett, Xia & Derrig, 1998), homicide (Kangas et al., 1999; Memon & Mehboob, 2006), mobile communications fraud (Hollmén, Tresp & Simula, 1999; Hollmén, 2000; Grosser, Britos & García-Martínez, 2005), murder and rape (Kangas, 2001), burglary (Adderley & Musgrave, 2003; Adderley, 2004), network intrusion (Axelsson, 2005; Lampinen, Koivisto & Honkanen, 2005; Leufven, 2006), cybercrime (Fei et al., 2005; Fei et al., 2006), and credit card fraud (Zaslavsky & Strizhak, 2006). This is the chief field where the application of the SOM has formerly been emphasized in the research associated with criminal justice.
Publishe: University of Tampere
By: Xingan Li, Henry Joutsijoki, Jorma Laurikkala, Martti Juhola
File Information: English Language/ 17Page / size:276KB
ناشر : University of Tampere
کاری از : Xingan Li, Henry Joutsijoki, Jorma Laurikkala, Martti Juhola
اطلاعات فایل : زبان انگلیسی /17 صفحه / حجم : 276KB
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