Crime Analysis using Open Source Information
This work provides a tool for analysts to efficiently understand crime patterns in specific areas, but it is incremental as it uses existing methods on new data.
The paper tackled crime analysis by applying unsupervised data mining techniques like clustering and association to open source information, achieving results comparable to manual analysis while saving significant time.
In this paper, we present a method of crime analysis from open source information. We employed un-supervised methods of data mining to explore the facts regarding the crimes of an area of interest. The analysis is based on well known clustering and association techniques. The results show that the proposed method of crime analysis is efficient and gives a broad picture of the crimes of an area to analyst without much effort. The analysis is evaluated using manual approach, which reveals that the results produced by the proposed approach are comparable to the manual analysis, while a great amount of time is saved.