Performance Analysis of Naïve Bayes Algorithm on Crime Data Using Rapid Miner

Waseem Ahmed, Dr. Siddhartha Sankar Biswas, Tabrez Nafis


Crime, when someone does any unlawful activity, the intensity level of crime can be from very low to very high. In current society, crime exist everywhere in distinct form, and if we collect all the data related to different crime, that data would be very large in volume which can be managed through data mining techniques. Using various data mining techniques, lot of conclusion can be drawn like rise or fall in particular type of crime, percentage of particular crime, time when crime mostly or less happens, area in which maximum or minimum crime happens etc. In this paper, we use rapid miner data mining tool and naïve Bayes classification algorithm to show the different types of crime. By using crime data on classification algorithm with rapid miner tool we tries to demonstrate that how efficiently naïve Bayes algorithm can manage this data and the accuracy of result. As there is a probability of crime prediction and naïve Bayes algorithm gives result based on probability, thus the help of naïve Bayes classification algorithm the better accuracy of result can be achieved. This research will be very useful in crime control and crime detection but these all depends on the volume and correctness of the data, more data gives the more accurate prediction.


Classification, Crime, data mining, naïve bayes, rapid miner

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