Abstract
Data analytics (DA) is defined as the process of scrutinizing different data sets to draw out the outcomes about the information they contain with the help of specialized functional systems and software. There are different areas where data analytics applications have been operated such as transportation, detection of fraud, city planning, health department, digital advertisement, etc. One of the key area of data analytics is in the crime world. Crime Analysis and prevention is a systematic approach for identifying and analyzing patterns and trends in crime. Our proposed method can predict regions which have high probability for a particular crime occurrence from previous years records and the necessary actions that can be taken place by the police authorities to provide more and more security. The necessary steps can be initiated for security reasons so that criminals think twice before performing a crime. Instead of focusing on causes of crime occurrence like criminal background of offender, etc., we are focusing on crime patterns in different regions. Crime Analysis is concerned with exploring different crime datasets, analyzing them and finding out certain patterns from them, so data analytics is a field which helps in establishing certain patterns from the data. In this paper, we are going to represent the crime data in the form of multipolarity to find relationships between the objects and the attributes. Since, the crime data is very large in size and in unstructured manner, so there is a need to first normalize the data and then find relationships among them by representing them in the form of m-Polar Fuzzy Contexts and m-Polar Fuzzy Concepts.
Published Version
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