Unexpectedness as a measure of interestingness in knowledge discovery
Decision Support Systems - Special issue on WITS '97
What Makes Patterns Interesting in Knowledge Discovery Systems
IEEE Transactions on Knowledge and Data Engineering
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Evaluation of Interestingness Measures for Ranking Discovered Knowledge
PAKDD '01 Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining
On Incorporating Subjective Interestingness Into the Mining Process
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Algorithms for estimating relative importance in networks
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Graph-based technologies for intelligence analysis
Communications of the ACM - Homeland security
ACM SIGKDD Explorations Newsletter
ACM SIGKDD Explorations Newsletter
Topological analysis of criminal activity networks: enhancing transportation security
IEEE Transactions on Intelligent Transportation Systems
CrimeLink explorer: using domain knowledge to facilitate automated crime association analysis
ISI'03 Proceedings of the 1st NSF/NIJ conference on Intelligence and security informatics
Untangling criminal networks: a case study
ISI'03 Proceedings of the 1st NSF/NIJ conference on Intelligence and security informatics
Dataset Analysis of Proxy Logs Detecting to Curb Propagations in Network Attacks
PAISI, PACCF and SOCO '08 Proceedings of the IEEE ISI 2008 PAISI, PACCF, and SOCO international workshops on Intelligence and Security Informatics
Using importance flooding to identify interesting networks of criminal activity
Journal of the American Society for Information Science and Technology
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In spite of policy concerns and high costs, the law enforcement community is investing heavily in data sharing initiatives. Cross-jurisdictional criminal justice information (e.g., open warrants and convictions) is important, but different data sets are needed for investigational activities where requirements are not as clear and policy concerns abound. The community needs sharing models that employ obtainable data sets and support real-world investigational tasks. This work presents a methodology for sharing and analyzing investigation-relevant data. Our importance flooding application extracts interesting networks of relationships from large law enforcement data sets using user-controlled investigation heuristics and spreading activation. Our technique implements path-based interestingness rules to help identify promising associations to support creation of investigational link charts. In our experiments, the importance flooding approach outperformed relationship-weight-only models in matching expert-selected associations. This methodology is potentially useful for large cross-jurisdictional data sets and investigations.