Publication Type:

Conference Paper


Networks Soft Computing (ICNSC), 2014 First International Conference on (2014)



Apriori algorithm, Artificial neural networks, Bayes methods, computerized system, crime data analysis, crime prevention, data analysis, Data mining, Decision Tree, GraphDB, law enforcement officers, Mongo DB, Naive Bayes, Neo4j, NER, police data processing, unstructured data, Visualization


Crime analysis and prevention is a systematic approach for identifying and analyzing patterns and trends in crime. Our system can predict regions which have high probability for crime occurrence and can visualize crime prone areas. With the increasing advent of computerized systems, crime data analysts can help the Law enforcement officers to speed up the process of solving crimes. Using the concept of data mining we can extract previously unknown, useful information from an unstructured data. Here we have an approach between computer science and criminal justice to develop a data mining procedure that can help solve crimes faster. Instead of focusing on causes of crime occurrence like criminal background of offender, political enmity etc we are focusing mainly on crime factors of each day.

Cite this Research Publication

Shiju Sathyadevan, S, D. M., and S., S. G., “Crime analysis and prediction using data mining”, in Networks Soft Computing (ICNSC), 2014 First International Conference on, 2014.