Scaling up dynamic time warping for datamining applications
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
On the need for time series data mining benchmarks: a survey and empirical demonstration
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering Similar Multidimensional Trajectories
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Shilling recommender systems for fun and profit
Proceedings of the 13th international conference on World Wide Web
Robust incentive techniques for peer-to-peer networks
EC '04 Proceedings of the 5th ACM conference on Electronic commerce
Finding group shilling in recommendation system
WWW '05 Special interest tracks and posters of the 14th international conference on World Wide Web
Robust and fast similarity search for moving object trajectories
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Preventing shilling attacks in online recommender systems
Proceedings of the 7th annual ACM international workshop on Web information and data management
Attack detection in time series for recommender systems
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
An efficient and accurate method for evaluating time series similarity
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Detecting Collusive Shill Bidding
ITNG '07 Proceedings of the International Conference on Information Technology
An Empirical Study of Collusion Behavior in the Maze P2P File-Sharing System
ICDCS '07 Proceedings of the 27th International Conference on Distributed Computing Systems
On the marriage of Lp-norms and edit distance
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Recommending trusted online auction sellers using social network analysis
Expert Systems with Applications: An International Journal
Collusion set detection using graph clustering
Data Mining and Knowledge Discovery
Proceedings of the VLDB Endowment
Domain-Driven Local Exceptional Pattern Mining for Detecting Stock Price Manipulation
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
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In financial markets, abnormal trading behaviors pose a serious challenge to market surveillance and risk management. What is worse, there is an increasing emergence of abnormal trading events that some experienced traders constitute a collusive clique and collaborate to manipulate some instruments, thus mislead other investors by applying similar trading behaviors for maximizing their personal benefits. In this paper, a method is proposed to detect the potential collusive cliques involved in an instrument of future markets by first calculating the correlation coefficient between any two eligible unified aggregated time series of signed order volume, and then combining the connected components from multiple sparsified weighted graphs constructed by using the correlation matrices where each correlation coefficient is over a user-specified threshold. Experiments conducted on real order data from the Shanghai Futures Exchange show that the proposed method can effectively detect suspect collusive cliques, which have been verified by financial experts. A tool based on the proposed method has been deployed in the exchange as a pilot application for futures market surveillance and risk management.