An Intelligent Analysis of Crime through Newspaper Articles - Clustering and Classification

Abstract

Crime analysis is one of the most important activities of the majority of the intelligent and law enforcement organizations all over the world. 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. A major challenge faced by most of the law enforcement and intelligence organizations is efficiently and accurately analyzing the growing volumes of crime related data. The vast geographical diversity and the complexity of crime patterns have made the analyzing and recording of crime data more difficult. This paper presents an intelligent crime analysis system which is designed to overcome the above mentioned problems. Data mining is used extensively in terms of analysis, investigation and discovery of patterns for occurrence of different crimes. The proposed system is a web-based system which performs crime analysis through news articles. In this paper we use a clustering/ classification based model to automatically group the retrieved documents into a list of meaningful categories. The data mining techniques are used to analyze the web data.