The detection and prevention of crime, in the past few decades, required several years of research and analysis. However, today, thanks to smart systems based on data mining techniques, it is possible to detect and prevent crime in a considerably less time. Classification and clustering-based smart techniques can classify and cluster the crime-related samples. The most important factor in the c...

Crime detection is one of the major issues in the field of criminology. In fact, criminology includes knowing the details of a crime and its intangible relations with the offender. In spite of the enormous amount of data on offenses and offenders, and the complex and intangible semantic relationships between this information, criminology has become one of the most important areas in the field o...

In this paper, a novel approach has been proposed to rank police administration units on the basis of their effective enforcement of crime prevention measures using Data Envelopment Analysis (DEA) and Clustering. The proposed approach will offer an effective mechanism not only to rank police administration units but also provide an evaluation tool to monitor the implementation of crime preventi...

traditional leveraging statistical methods for analyzing today’s large volumes of spatial data have high computational burdens. to eliminate the deficiency, relatively modern data mining techniques have been recently applied in different spatial analysis tasks with the purpose of autonomous knowledge extraction from high-volume spatial data. fortunately, geospatial data is considered a proper s...

Statistical clustering of criminal events can be used by crime analysts to create of lists of potential suspects for an unsolved crime, identify groups of crimes that may have been committed by the same individuals or group of individuals, for offender profiling, and for predicting future events. In this paper, we propose a Bayesian model-based clustering approach for criminal events. Our appro...

This paper suggests a novel clustering method for analyzing the National Incident-Based Reporting System (NIBRS) data, which include the determination of correlation of different crime types, the development of a likelihood index for crimes to occur in a jurisdiction, and the clustering of jurisdictions based on crime type. The method was tested by using the 2005 assault data from 121 jurisdict...

Recently, interest has been increasing in crime analytics in which time series clustering research has been playing an important role, particularly for finding useful similar trends in crime. Time series clustering technique has potential for large volume of data analysis. Multivariate time series data relating to various areas such as finance, health sector, environmental research, and crime i...

This study presents the results of an experimental study of two document clustering techniques which are kmeans and k-means++. In particular, we compare the two main approaches in crime document clustering. The drawback of k-means is that the user needs to define the centroid point. This becomes more critical when dealing with document clustering because each center point represented by a word ...

Law enforcement agencies use various crime analysis tools to discover crimes. However, a big volume of crime data has made the process of analyzing crimes difficult. Previously proposed methods only consider improving the productivity of the detectives and other law enforcement agencies. In this paper, we argue that the crime analysis methods should be also useful for citizen, and propose a use...