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 navigation history for recommendation
Proceedings of the 5th international conference on Intelligent user interfaces
Efficient Adaptive-Support Association Rule Mining for Recommender Systems
Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
CrimeNet explorer: a framework for criminal network knowledge discovery
ACM Transactions on Information Systems (TOIS)
Constructing Bayesian networks for criminal profiling from limited data
Knowledge-Based Systems
Effect of Inventor Status on Intra-Organizational Innovation Evolution
HICSS '09 Proceedings of the 42nd Hawaii International Conference on System Sciences
TrustWalker: a random walk model for combining trust-based and item-based recommendation
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Using a trust network to improve top-N recommendation
Proceedings of the third ACM conference on Recommender systems
Untangling criminal networks: a case study
ISI'03 Proceedings of the 1st NSF/NIJ conference on Intelligence and security informatics
A matrix factorization technique with trust propagation for recommendation in social networks
Proceedings of the fourth ACM conference on Recommender systems
Locating Central Actors in Co-offending Networks
ASONAM '11 Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining
A survey of collaborative filtering based social recommender systems
Computer Communications
| Hi-index | 0.00 |
Law enforcement and intelligence agencies have long realized that analysis of co-offending networks, networks of offenders who have committed crimes together, is invaluable for crime investigation, crime reduction and prevention. Investigating crime can be a challenging and difficult task, especially in cases with many potential suspects and inconsistent witness accounts or inconsistencies between witness accounts and physical evidence. We present here a novel approach to crime suspect recommendation based on partial knowledge of offenders involved in a crime incident and a known co-offending network. To solve this problem, we propose a random walk based method for recommending the top-K potential suspects. By evaluating the proposed method on a large crime dataset for the Province of British Columbia, Canada, we show experimentally that this method outperforms baseline random walk and association rule-based methods. Additionally, results obtained for public domain data from experiments for co-author recommendation on a DBLP co-authorship network are consistent with those on the crime dataset. Compared to the crime dataset, the performance of all competitors is much better on the DBLP dataset, confirming that crime suspect recommendation is an inherently harder task.