Exploring Women's Safety: Uncovering Crime Patterns through Fuzzy Clustering Methods

Abstract

The escalating frequency of crimes against women worldwide underscores the imperative to identify and implement effective measures to mitigate such incidents. In order to protect women from victimization, it is necessary to analyze crime trends systematically to discern patterns that can serve as valuable precautionary measures. In contemporary studies focused on crime analysis, conventional hard clustering techniques are commonly employed to assess the prevalence of criminal activities in specific regions. However, these clustering methods, grounded in crisp set theory, encounter challenges in handling partial membership, making it difficult to identify regions exhibiting partial affiliation with multiple clusters characterized by varying crime intensities. Recognizing the constraints associated with hard clustering techniques, we aim to employ a fuzzy clustering approach to a dataset related to crimes against women, with the objective of elucidating significant patterns within the data.