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：670-674
语种：English
出版社：Ayushmaan Technologies
摘要：Crime analysis uses past crime data to predict future crime locations and times. Criminology is an area that focuses the scientific study of crime and criminal behavior. It is a process that aims to identify crime characteristics. It is one of the most important fields where the applications of data mining techniques can produce important results. The exponentially increasing amounts of data being generated each year make getting useful information from that data more and more critical.. Analysis of the data includes simple query and reporting, statistical analysis, more complex multidimensional analysis, and data mining. The wide range of data mining applications has made it an important field of research. Criminology is one of the most important fields for applying data mining. Criminology is a process that aims to identify crime patterns. The high volume of crime datasets and also the complexity of relationships between these kinds of data have made criminology an appropriate field for applying data mining techniques. An approach based on data mining techniques is discussed in this paper to extract important patterns from reports gathered from the city police department. The reports are written in simple plain text. The plain texts are converted into the format understandable by the tool. Then, exiting data mining techniques are applied to get patterns of crime data and a new algorithm is proposed to improve the accuracy of the crime pattern detection system. The various data mining techniques such as clustering and classification are used to get the patterns of crime data. This paper presents a new algorithm for K-Means using weighted approach. The results of proposed algorithm are compared with existing K-means clustering algorithm. The weighted approach proves to be better approach than existing K-means.
