Paper Contents
Abstract
Contemporary India faces escalating criminal activities that leverage modern technological capabilities and digital platforms to execute sophisticated offenses. The complexity of criminal behavior patterns necessitates advanced analytical approaches that can process large-scale datasets and identify meaningful trends. This research presents an enhanced clustering methodology based on K-means algorithms to analyze criminal activities and predict regional crime distribution patterns. Our investigation centers on developing predictive models that can identify geographical areas with elevated criminal activity levels and demographic segments exhibiting varying degrees of criminal involvement. The study implements an optimized K-means clustering approach that reduces computational complexity while enhancing analytical accuracy. The proposed methodology demonstrates improved efficiency in processing crime-related datasets and generating actionable intelligence for law enforcement agencies.
Copyright
Copyright © 2025 Mrs. B. Mamatha. This is an open access article distributed under the Creative Commons Attribution License.
