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An Intelligent Document Clustering Approach to Detect Crime Patterns

Procedia Technology 2013年 11卷 1181-1187页

作者： Qusay Bsoul Juhana Salim Lailatul Qadri Zakaria Knowledge Technology Research Group Universiti Kebangsaan Malaysia 43600 Bangi Selangor Malaysia

A wide number of reports and news on crimes, increasingly conducted almost every day, have resulted in making detection of such crimes more difficult if not complex. Therefore, the need for detecting and identifying such crimes emerges as a necessary way of detecting and identifying such crime patterns on the news. Document Clustering have been increasingly becoming an important task for obtaining good results with the unsupervised learning methods. It aims to automatically group similar documents in one cluster using different types of extractions and cluster algorithms. There are ongoing works done to improve Document Clustering techniques such as Extractions and Clustering approaches to overcome the difficulty in designing a general purpose document clustering for crime investigation and the ill posed problem of extraction and clustering. This paper discusses two major sequential stages in Document Clustering “Extraction Features and Clustering algorithms” as well as the major challenges and the key issues in designing extraction features and clustering algorithms. In addition, the following approach assists the law enforcement officers and detectives to enhance performance and speed up the process of solving crimes.

关键词： Intelligent Clustering K-means Affinity Propagation algorithm lemmatization algorithm Crime Detection

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