Folksonomy-Based Collabulary Learning
ISWC '08 Proceedings of the 7th International Conference on The Semantic Web
AFOPT-tax: an efficient method for mining generalized frequent itemsets
ACIIDS'10 Proceedings of the Second international conference on Intelligent information and database systems: Part I
Mining RDF metadata for generalized association rules
DEXA'06 Proceedings of the 17th international conference on Database and Expert Systems Applications
Generalized association rule mining with constraints
Information Sciences: an International Journal
Mining probabilistic generalized frequent itemsets in uncertain databases
Proceedings of the 51st ACM Southeast Conference
Personalized tag recommendation based on generalized rules
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Section on Intelligent Mobile Knowledge Discovery and Management Systems and Special Issue on Social Web Mining
Misleading Generalized Itemset discovery
Expert Systems with Applications: An International Journal
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Generalized association rule mining is an extension of traditional association rule mining to discover more informative rules,given a taxonomy.In this paper, we describe a formal framework for the problem of mining generalized association rules.In the framework, Thesubset-superset and the parent-child relationships among generalized itemsets are introduced to present the different views of generalized itemsets, i.e. the lattice of generalized itemsets and the taxonomies of k-generalized itemsets ,respectively. We present an optimizationtechnique to reduce the time consuming by applying two constraints each of hich corresponds to each view of generalized itemsets.In the mining process, a new set enumeration algorithm, named SET, that utilizes these constraints to fasten mining all generalized frequent itemsets is proposed. By experiments on synthetic data, the results show that SET outperforms the current most efficient algorithm, Prutax, by an order of magnitude or more.