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Title:
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ARCA: MINING CRIME PATTERNS USING ASSOCIATION RULES |
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Author(s):
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Bruno Laporais Pereira, Wladmir Cardoso Brandão |
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ISBN:
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978-989-8533-25-8 |
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Editors:
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Hans Weghorn |
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Year:
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2014 |
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Edition:
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Single |
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Keywords:
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Knowledge Discovery, Crime Data Mining, Crime Patterns, Association Rules. |
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Type:
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Full Paper |
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First Page:
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159 |
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Last Page:
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166 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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Crime is a social-economic problem affecting people around the world and negatively impacting on society welfare. Law enforcement agencies need to formulate crime policies and strategic plans to prevent and reduce crime. However, they face the challenge to effectively extract relevant knowledge from a large volume of criminal data and reports. In this article, we introduce ARCA, a novel approach to discover crime patterns from real crime datasets by using association rules mining. The Apriori algorithm used by our approach can recognizes mutual implications among criminal occurrences, retrieving relevant information on criminal behavior. ARCA provides a multidimensional data model suitable for crime event analysis, using association rules techniques to address the crime data mining problem. Additionally, we evaluate our approach using a real crime dataset, attesting its usefulness to optimize the allocation of law enforcement resources by improving the productivity of the law enforcement officers to prevent and reduce crime. |
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