Crime Hot-Spots Prediction Using Support Vector Machine

Location prediction is a special case of spatial data mining classification. For instance, in the public safety domain, it may be interesting to predict location(s) of crime hot spots. In this study, we present Support Vector Machine (SVM) based approach to predict the location as alternative to existing modeling approaches. SVM forms the new generation of machine learning techniques used to find optimal separability between classes within datasets. Experiments on two different spatial datasets show that SVMs gives reasonable results.

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