Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Mining fuzzy association rules in databases
ACM SIGMOD Record
Mining association rules with multiple minimum supports
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Constraint-Based Rule Mining in Large, Dense Databases
Data Mining and Knowledge Discovery
Mining association rules on significant rare data using relative support
Journal of Systems and Software
Extracting meaningful entities from police narrative reports
dg.o '02 Proceedings of the 2002 annual national conference on Digital government research
Data Mining Approaches to Criminal Career Analysis
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Crime Pattern Detection Using Data Mining
WI-IATW '06 Proceedings of the 2006 IEEE/WIC/ACM international conference on Web Intelligence and Intelligent Agent Technology
Incremental mining for temporal association rules for crime pattern discoveries
ADC '07 Proceedings of the eighteenth conference on Australasian database - Volume 63
An Analysis of Data Mining Applications in Crime Domain
CITWORKSHOPS '08 Proceedings of the 2008 IEEE 8th International Conference on Computer and Information Technology Workshops
Mining top-k and Bottom-k correlative crime patternsthrough graph representations
ISI'09 Proceedings of the 2009 IEEE international conference on Intelligence and security informatics
Where do I start?: algorithmic strategies to guide intelligence analysts
Proceedings of the ACM SIGKDD Workshop on Intelligence and Security Informatics
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Current manual inspection of crime data by analysts is limited, primarily due to the amount of data that can be processed concurrently and in a reasonable time frame. Further, complex relationships between various crime attributes can be overlooked by human analysts. Providing automated knowledge discovery tools becomes attractive to accelerate the efforts of local law enforcement. In this paper, we study the application of fuzzy association rule mining for community crime pattern discovery. Discovered rules are presented and discussed at regional and national levels. Rules found to hold in all states, be consistent across all regions, and subsets of regions are also discussed. A relative support metric was defined to extract rare, novel rules from thousands of discovered rules. Such an approach relieves the need of law enforcement personnel to sift through uninteresting, obvious rules in order to find interesting and meaningful crime patterns of importance to their community.