Indian Journal of Science and Technology
Year: 2015, Volume: 8, Issue: 34, Pages: 1-9
S. Sivaranjani* and S. Sivakumari
Objective: The main objective of this research is to reduce the burden of crime investigators by identifying the series of crimes happening at different places. And also, this work aims to reduce the investigation time by grouping similar crimes happened in different places based on its behavior with the consideration of the class imbalance problem. Methods: In this research, Majority Weighted Class Oversamplingand Modified Cut Clustering (MWMO-MCC) method is introduced to identify hot spots of serial crime location. MWMO technique is used to handle the class imbalance problem which occurs due to varying size of different crime data’s. MCC algorithm is introduced to handle the insertion and deletion operations where the existing methodology called graph cut clustering algorithm cannot handle these problems in case of dynamic growth of data’s. The proposed methodologies deal with the class imbalance problems effectively and also the modification processes over the partitioned graphs are supported well than the existing researches. Results: The proposed methodology of this research work namely MWMO-MCC is used to detect the hot spots of serial crimes by identifying the similarity relationship exists among the crimes happened in different places. The experimental tests conducted were proves that the proposed methodology can leads to well detection of serial residential crimes than the existing methodologies. The performance evaluation of this research work proves that the proposed methodology is improved in terms of performance metrics called jaccard index, mantel index and the journey distance time. Conclusion: The findings of this research work demonstrate that hot spots of serial residential crimes are identified by clustering them effectively using the proposed research methodology called MWMO-MCC and it has high possibility of detection of crimes than the existing methodology.
Keywords: Cut Clustering, Incremental Clustering, Majority Weighted Class Oversampling, Serial Crimes
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