Exploration of Massive Crime Data Sets through Data Mining Techniques
Author(s)
Lee, Ickjai
Estivill-Castro, Vladimir
Griffith University Author(s)
Year published
2011
Metadata
Show full item recordAbstract
We incorporate two data mining techniques, clustering and association-rule mining, into a fruitful exploratory tool for the discovery of spatio-temporal patterns in data-rich environments. This tool is an autonomous pattern detector that efficiently and effectively reveals plausible cause-effect associations among many geographical layers. We present two methods for exploratory analysis and detail algorithms to explore massive databases. We illustrate the algorithms with real crime data sets.We incorporate two data mining techniques, clustering and association-rule mining, into a fruitful exploratory tool for the discovery of spatio-temporal patterns in data-rich environments. This tool is an autonomous pattern detector that efficiently and effectively reveals plausible cause-effect associations among many geographical layers. We present two methods for exploratory analysis and detail algorithms to explore massive databases. We illustrate the algorithms with real crime data sets.
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Journal Title
Applied Artificial Intelligence
Volume
25
Issue
5
Subject
Cognitive and computational psychology