Propose Data Mining AR-GA Model to Advance Crime analysis
Journal Title: IOSR Journals (IOSR Journal of Computer Engineering) - Year 2013, Vol 14, Issue 5
Abstract
Historically solving crimes has been the privilege of the criminal justice and law enforcement specialists. With the increasing use of the computerized systems to track crimes, computer data analysts have started helping the law enforcement officers and detectives to speed up the process of solving crimes. According to, solving crimes is a complex task that requires human intelligence and experience. In this research we belief data mining is a technique that can assist law enforcement officers with crime detection problems, so the proposal tries to benefits years of human experience into computer models via data mining. Here we will take an interdisciplinary approach between computer science and criminal justice to develop a proposed data mining model. The proposed model is a three correlated dimensional model; each dimension is a datasets, first one present crime dataset second present criminal dataset and the third present geo-crime dataset. This model apply the Association Rules AR data mining algorithm on each of the three correlated dataset separately then using Genetic Algorithm GA as mixer of the resulted ARs to exploit the relational patterns among crime, criminal and geo-crime to help to detect universal crimes patterns and speed up the process of solving crime with more accurate. This research introduces suggestion to secure the results of the data mining association rules. For privacy preserving secure datasets we aim to hide the general secure and sensitive rules from appearing as a result of applying AR. This could be done by making the confidence of secure rules equal to zero by modifying the supports of critical and sensitive items in these rules. The proposal applied on real crime data from a dependable sheriff’s office and validated our results
Authors and Affiliations
Emad K. Jabar
Keywords
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