Crime Detection Technique Using Data Mining and K-Means

Abstract

Abstract-Crimes will somehow influence organizations and institutions when occurred frequently in a society. Thus, it seems necessary to study reasons, factors and relations between occurrence of different crimes and finding the most appropriate ways to control and avoid more crimes. The main objective of this paper is to classify clustered crimes based on occurrence frequency during different years. Data mining is used extensively in terms of analysis, investigation and discovery of patterns for occurrence of different crimes. We applied a theoretical model based on data mining techniques such as clustering and classification to real crime dataset recorded by police in England and Wales within 1990 to 2011. We assigned weights to the features in order to improve the quality of the model and remove low value of them. The Genetic Algorithm (GA) is used for optimizing of Outlier Detection operator parameters using RapidMiner tool.

Geographic Information Systems for Crime Prone Areas Clustering

“…Subsequent research crime prediction using K-means Clustering [3], this research show common crime patterns and crime prone areas for future prediction. The next research crime analysis using K-means Clustering [4], this research was made cluster and trend crime data using rapidminer. The next research is discussed the analysis of the k-means method in grouping crime by region he next research is discussed the analysis of the k-means method in grouping crime by region [5], in this study the grouping of regions was based on 2 clusters, namely high and low, but in this study the mapping process was not carried out and the data used were still static .…”