Alternative Interest Measures for Mining Associations in Databases
IEEE Transactions on Knowledge and Data Engineering
CoMine: Efficient Mining of Correlated Patterns
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
On Mining Instance-Centric Classification Rules
IEEE Transactions on Knowledge and Data Engineering
Efficient Mining of Frequent Patterns from Uncertain Data
ICDMW '07 Proceedings of the Seventh IEEE International Conference on Data Mining Workshops
DTU: A Decision Tree for Uncertain Data
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
A Rule-Based Classification Algorithm for Uncertain Data
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Frequent pattern mining with uncertain data
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Probabilistic frequent itemset mining in uncertain databases
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining frequent itemsets from uncertain data
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Direct mining of discriminative patterns for classifying uncertain data
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
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One of the most important tasks in data mining is to discover associations and correlations among items in a huge database. In recent years, some studies have been conducted to find a more accurate measure to describe correlations between items. It has been proved that the newly developed measures of all-confidence and bond perform much better in reflecting the true correlation relationship than just using support and confidence in categorical database. Hence, several efficient algorithms have been proposed to mine correlated patterns based on all-confidence and bond. However, as the data uncertainty become increasingly prevalent in various kinds of real-world applications, we need a brand new method to mine the true correlations in uncertain datasets with high efficiency and accuracy. In this paper, we propose effective methods based on dynamic programming to compute the expected all-confidence and expected bond, which could serve as a slant in finding correlated patterns in uncertain datasets.