Elements of information theory
Elements of information theory
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Introduction to Algorithms
Alternative Interest Measures for Mining Associations in Databases
IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Finding Representative Set from Massive Data
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Mining quantitative correlated patterns using an information-theoretic approach
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Maximally informative k-itemsets and their efficient discovery
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Data Mining and Knowledge Discovery
A First Course in Information Theory (Information Technology: Transmission, Processing and Storage)
A First Course in Information Theory (Information Technology: Transmission, Processing and Storage)
Finding low-entropy sets and trees from binary data
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Sample Selection for Maximal Diversity
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Mining non-redundant high order correlations in binary data
Proceedings of the VLDB Endowment
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The application of feature subsets with high order correlation in classification has demonstrates its power in a recent study, where non-redundant interacting feature subsets (NIFS) is defined based on multi-information. In this paper, we re-examine the problem of finding NIFSs. We further improve the upper bounds and lower bounds on the correlations, which can be used to significantly prune the search space. The experiments on real datasets demonstrate the efficiency and effectiveness of our approach.