Cluster analysis for reducing city crime rates

Abstract

Data analysis plays an indispensable role in the knowledge discovery process of extracting of interesting patterns or knowledge for understanding various phenomena or wide applications. Visual Data Mining is further presenting implicit but useful knowledge from large data sets using visualization techniques, to create visual images which aid in the understanding of complex, often massive representations of data. As the amount of data managed in a database increases, the need to simplify the vast amount of data also increases. Cluster analysis is the process of classifying a large group of data items into smaller groups that share the same or similar properties. In this paper, different Clustering algorithms such as K-Means clustering, agglomerative clustering were studied and applied to the Stop, Question and Frisk Report Database, City of New York, Police Department, NYPD, for analyzing the location of the crime and stopped people using the reason of stopped in order to reduce city crime rates. Our analytic and visual results revealed that the best clustering algorithm is K-Means algorithm, and its good features ensuring that the models are helpful.