Evaluating Performances of Two Unsupervised Methods for ‎Classification Crime Text

Abstract

Crime is a pervasive societal issue that has a negative impact on both a community's economic growth and overall quality of life. The bulk of crimes committed in everyday times are documented online by the wherewithal of news reports, blogs, and social networking sites. To improve crime analytics and community protection in response to rising crime. Law enforcement agencies continue to promote effective electronic information systems and crime data mining. Consequently, the aim of this study is to design a system of crime that depends on unsupervised machine-learning techniques that categorize five types of crime text. Two famous unsupervised algorithms: Independent Component Analysis based on natural gradient (NG-ICA) and Fast Independent Component Analysis (Fast-ICA) were used, to recover the latent components from observations. In order to evaluate the proposed system, the BERNAMA dataset, which had been manually annotated was used. Two experiments were conducted, and the results showed that the approaches that were employed satisfied promising results. Where the NG-ICA achieved an average F-measure of 83.3% and the Fast-ICA achieved 87.1%. This outcome demonstrates the appropriateness of these techniques in the implementation of text mining.