Mining large data set is an important issue to deal with as data is growing as the field grows. Today, crime rate is a menace that each country faces. With the increase in crime rate the data is increasing and it is such a critical field that accuracy is important at the same time. This paper shows the comparison in the results between clustering and the classification. K means is used in clust...

Concern about national security has increased after the 26/11 Mumbai attack. In this paper we look at the use of missing value and clustering algorithm for a data mining approach to help predict the crimes patterns and fast up the process of solving crime. We will concentrate on MV algorithm and Apriori algorithm with some enhancements to aid in the process of filling the missing value and iden...

Crimes are a social nuisance and cost our society dearly in several ways. Crime investigation has very significant role of police system in any country. Developing a good crime analysis tool to identify crime patterns quickly and efficiently for future crime pattern detection is required. This paper presents combine approach of clustering, outlier detection and providing the rule engine to iden...

Can crimes be modeled as data mining problems? We will try to answer this question in this paper. Crimes are a social nuisance and cost our society dearly in several ways. Any research that can help in solving crimes faster will pay for itself. Here we look at use of clustering algorithm for a data mining approach to help detect the crimes patterns and speed up the process of solving crime. We ...

Data mining is a way to extract knowledge out of usually large data sets; in other words it is an approach to discover hidden relationships among data by using artificial intelligence methods. The wide range of data mining applications has made it an important field of research. Criminology is one of the most important fields for applying data mining. Criminology is a process that aims to ident...

Objectives The classification of crime into discrete categories entails a massive loss of information. Crimes emerge out of a complex mix of behaviors and situations, yet most of these details cannot be captured by singular crime type labels. This information loss impacts our ability to not only understand the causes of crime, but also how to develop optimal crime prevention strategies. Methods...

Serial crimes are the problems which are mostly committed by minority of offenders. The law enforcement people are forced to find out the serial crime which is considered as complex task. In order to find out the serial crimes, one needs to investigate the large number of crimes which are unlinked with each other. These problem need to be analyzed in different ways to find out the link that exi...

Highly clustered event sequences are observed in certain types of crime data, such as burglary and gang violence, due to crime-specific patterns of criminal behavior. Similar clustering patterns are observed by seismologists, as earthquakes are well known to increase the risk of subsequent earthquakes, or aftershocks, near the location of an initial event. Space–time clustering is modeled in se...

Our study aims to build a machine learning model for crime prediction using geospatial features for different categories of crime. The reverse geocoding technique is applied to retrieve open street map (OSM) spatial data. This study also proposes finding hotpoints extracted from crime hotspots area found by Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN). A sp...