Data mining: concepts and techniques
Data mining: concepts and techniques
Mining Both Positive and Negative Association Rules
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Mining for Strong Negative Associations in a Large Database of Customer Transactions
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Unified Algorithm for Undirected Discovery of Execption Rules
PKDD '00 Proceedings of the 4th European Conference on Principles of 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
Exception Rule Mining with a Relative Interestingness Measure
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
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This paper addresses the problem of mining exceptions from multidimensional databases The goal of our proposed model is to find association rules that become weaker in some specific subsets of the database The candidates for exceptions are generated combining previously discovered multidimensional association rules with a set of significant attributes specified by the user The exceptions are mined only if the candidates do not achieve an expected support We describe a method to estimate these expectations and propose an algorithm that finds exceptions Experimental results are also presented.