Detection of Crimes using Unsupervised Learning Techniques

Objectives: The main objective of this paper is to solve the criminal problems with in less amount of time. There are many methods to do so but this paper concentrates in solve the easily and reduce the time in solving the case. Methods: To solve the criminal cases with in less time there are many methods but here we used clustering technique. Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). When a case is enrolled into the data base before if there is any case similar to it then we can solve the case easily by doing the same procedure. Findings: Before they used to file a case on FIR. But now a day, they are using data bases to file a case. By getting any new case they are comparing the new case with the older case so that it will be easy to find the suspect as it takes less time to solve the case. Before they used for other techniques like classification etc. But in my findings and research work clustering is simple, more accurate and takes less time to solve the case easily. In clustering techniques also we have different type of algorithm, but in this paper we are using the k-means algorithm and expectation - maximization algorithm. We are using these techniques because these two techniques come under the partition algorithm. Partition algorithm is one of the best method to solve crimes and to find the similar data and group it. K-means algorithm is done by partitioning data into groups based on their means. K-means algorithm has an extension called expectation- maximization algorithm here we partition the data based on their parameters. Applications: This system can be used for the Indian crime departments for reducing the crime and solving the crimes with less time. This technique can be used to solve the crimes with in less time.