Fast discovery of association rules
Advances in knowledge discovery and data mining
Algorithms for Mining Distance-Based Outliers in Large Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
An Interval Classifier for Database Mining Applications
VLDB '92 Proceedings of the 18th International Conference on Very Large Data Bases
Efficient Mining of Association Rules in Large Dynamic Databases
BNCOD 16 Proceedings of the 16th British National Conferenc on Databases: Advances in Databases
Finding surprising patterns in a time series database in linear time and space
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient dynamic mining of constrained frequent sets
ACM Transactions on Database Systems (TODS)
CanTree: A Tree Structure for Efficient Incremental Mining of Frequent Patterns
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
MaxDomino: efficiently mining maximal sets
BNCOD'03 Proceedings of the 20th British national conference on Databases
Design and development of a prototype system for detecting abnormal weather observations
Proceedings of the 2008 C3S2E conference
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In this paper, we develop an efficient system to detect outliers from real-life financial time series comprising of security prices. Our system consists of a data mining algorithm and a statistical algorithm. When applying each of these two algorithms individually, we observed its strengths and weaknesses. To overcome the weaknesses of the two algorithms, we combine the algorithms together. By so doing, we efficiently detect outliers from the financial time series. Moreover, the resulting (processed) datasets can then be used as input for some financial models used in forecasting future security prices or in predicting future market behaviour. This shows an alternative role of our outlier detection system—serving as a pre-processing step for other financial models.