Abstract
One of the most alarming and predominant aspects in our society is crime. Deterrence against crime is vital for the safety of each citizen of a nation. Analysis of crime in a systematic pattern can enable us to follow the trend of crime occurring and help us to prevent adversity. The main focus of this paper is to analyze the patterns of the data collected over a period of time about the crime against women by applying various clustering algorithms such as K-Means Custering, Agglomerative Custering, and Density-Based Spatial Clustering with Noise (DBSCAN). Clustering techniques are used massively along with the analysis, investigation, and uncovering the patterns for occurrence of different data. The comparative analysis of K-Means, Agglomerative Hierarchical Custering (AHC), and Density-Based Spatial Clustering (DBSCAN) were classified by training and test the real-time crime data against women were collected from the West Bengal, one of the famous state in India. The comparative analysis shows that the accuracy has been achieved as 96.1% of accuracy using DBSCAN clustering technique. This can help the police force to predict crimes which can occur in the future and take steps for accurate prevention.
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Anitha, A. (2020). Prediction of Crime Rate Using Data Clustering Technique. In: Das, K., Bansal, J., Deep, K., Nagar, A., Pathipooranam, P., Naidu, R. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 1048. Springer, Singapore. https://doi.org/10.1007/978-981-15-0035-0_35
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DOI: https://doi.org/10.1007/978-981-15-0035-0_35
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