Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Parallel Mining of Association Rules
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
IEA/AIE '00 Proceedings of the 13th international conference on Industrial and engineering applications of artificial intelligence and expert systems: Intelligent problem solving: methodologies and approaches
A Dynamic Approach for Knowledge Discovery of Web Access Patterns
ISMIS '00 Proceedings of the 12th International Symposium on Foundations of Intelligent Systems
Finding Maximal Frequent Itemsets over Online Data Streams Adaptively
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Decisions: algebra and implementation
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
The augmented itemset tree: a data structure for online maximum frequent pattern mining
DS'11 Proceedings of the 14th international conference on Discovery science
An automata approach to pattern collections
KDID'04 Proceedings of the Third international conference on Knowledge Discovery in Inductive Databases
Implicit enumeration of patterns
KDID'04 Proceedings of the Third international conference on Knowledge Discovery in Inductive Databases
Min-Max itemset trees for dense and categorical datasets
ISMIS'12 Proceedings of the 20th international conference on Foundations of Intelligent Systems
ISMIS'12 Proceedings of the 20th international conference on Foundations of Intelligent Systems
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Enhancements in data capturing technology have lead to exponential growth in amounts of data being stored in information systems. This growth in turn has motivated researchers to seek new techniques for extraction of knowledge implicit or hidden in the data. In this paper, we motivate the need for an incremental data mining approach based on data structure called the item-set tree. The motivated approach is shown to be effective for solving problems related to efficiency of handling data updates, accuracy of data mining results, processing input transactions, and answering user queries. We present efficient algorithms to insert transactions into the item-set tree and to count frequencies of itemsets for queries about strength of association among items. We prove that the expected complexity of inserting a transaction is ≅ O(1), and that of frequency counting is O(n), where n is the cardinality of the domain of items.