Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Rule Induction with CN2: Some Recent Improvements
EWSL '91 Proceedings of the European Working Session on Machine Learning
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Intelligent Rollups in Multidimensional OLAP Data
Proceedings of the 27th International Conference on Very Large Data Bases
Regression Cubes with Lossless Compression and Aggregation
IEEE Transactions on Knowledge and Data Engineering
Fast Rollup on Recursive Hierarchy in OLAP
WI '06 Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence
Multi-dimensional regression analysis of time-series data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Compression and Aggregation for Logistic Regression Analysis in Data Cubes
IEEE Transactions on Knowledge and Data Engineering
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In the process of OLAP analysis based on multi-dimensional data, analysts are often involved in large-scale data cube, which results users cannot find the interest information efficiently. To overcome this problem, some exceptions mining or exceptions-based methods were proposed. In this paper, a new regression-based definition of exception is proposed, threshold exception, andfollowing which an exception mining algorithm is proposed to help users find the exceptions in the data cells effectively using regression parameters. This method estimates the data as exception by comparing its normalized residual to the thresholds user gave. Performance study shows that the method is practical and effective.