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.
Highlights
Optimization of Outlier Detection operator parameters through the Genetic Algorithm (GA) and definition of a Fitness function are both based on Accuracy and Classification error
The weighting method was used to eliminate low-value features because such data reduce the quality of data clustering and classification and, reduce the prediction accuracy and increase the classification error
It can be said that the clustering is equal to the classification, with only difference that the classes are not defined and determined in advance, and grouping of the data is done without supervision [2]
Summary
Collection and analysis of crime-related data are imperative to security agencies. The use of a coherent method to classify these data based on the rate and location of occurrence, detection of the hidden pattern among the committed crimes at different times, and prediction of their future relationship are the most important aspects that have to be addressed. In this regard, the use of real datasets and presentation of a suitable framework that does not be affected by outliers should be considered. The weighting method was used to eliminate low-value features because such data reduce the quality of data clustering and classification and, reduce the prediction accuracy and increase the classification error. The main purposes of crime analysis are mentioned below [1]: Extraction of crime patterns by crime analysis and based on available criminal information, Prediction of crimes based on spatial distribution of existing data and prediction of crime frequency using various data mining techniques, Crime recognition
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