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@article{146524,
        author = {Richa A Patel and kinjal thakar and rina raval},
        title = {cluster based zoning for crime information.},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {4},
        number = {12},
        pages = {985-990},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=146524},
        abstract = {Crime is an old as mankind itself in around the world. Due to the huge dataset, Prediction of crime status is highly complex. Crime status prediction is associated with types of crime in particular area, which is helpful to security department such as how to distribute police in particular areas. For Crime related solutions, many algorithm based solutions have been published. Classification and clustering algorithm are two of them, who provide guidelines with recommendations to assist decision making, accurate for people at risk. Naive Bayesian (0.898), k-Nearest Neighbor (k-NN) (0.895) and Neural Networks (0.892) were selected as the basic data mining algorithms for this process. To improve the accuracy and of the system, Support Vector Machine algorithm has been used. Furthermore, the performance of mining result is improved by using chi-square feature selection technique. Created custom matrix is divided on cluster wise zoning map. },
        keywords = {data mining, Crime dataset, Cluster zone,Naïve Bayesian, k-NN, SVM},
        month = {},
        }