期刊名称：International Journal of Computer Science & Technology
印刷版ISSN：2229-4333
电子版ISSN：0976-8491
出版年度：2013
卷号：4
期号：2
页码：289-292
语种：English
出版社：Ayushmaan Technologies
摘要：This survey paper categorizes, compares, and summarizes the dataset, algorithm and performance measurements in almost all published technical and review articles in automated crime pattern detection. Crime is classically “unpredictable”. It is not necessarily random, but neither does it take place consistently in space and time. A better theoretical understanding is needed to facilitate practical crime prevention solutions that correspond to specific places and times. Crime analysis uses past crime data to predict future crime locations and time. The retrieved literature used mining algorithms including statistical test, regression analysis, neural networks, decision tree, Bayesian networks etc. For any kind of crime pattern detection commonly used data mining techniques includes clustering and classification techniques. Generally the detecting effect and accuracy of neural networks are superior than other classification models. General conclusion is that improvement in clustering can improve the classifier evaluation. There is a need to introduce other algorithm for improving clustering techniques. Owing to the size of data samples, some literature reached conclusion based on training samples.
