Mining association rules with multiple minimum supports
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Quantifying the utility of the past in mining large databases
Information Systems
Real world performance of association rule algorithms
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Discovery of Multiple-Level Association Rules from Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Towards personalized recommendation by two-step modified Apriori data mining algorithm
Expert Systems with Applications: An International Journal
Discovery of unapparent association rules based on extracted probability
Decision Support Systems
Mining periodic-frequent patterns with maximum items' support constraints
Proceedings of the Third Annual ACM Bangalore Conference
Improved approaches to mine rare association rules in transactional databases
Proceedings of the Fourth SIGMOD PhD Workshop on Innovative Database Research
Towards efficient mining of periodic-frequent patterns in transactional databases
DEXA'10 Proceedings of the 21st international conference on Database and expert systems applications: Part II
Using data mining techniques to predict hospitalization of hemodialysis patients
Decision Support Systems
Proceedings of the 14th International Conference on Extending Database Technology
From data to global generalized knowledge
Decision Support Systems
Mining rare association rules in the datasets with widely varying items' frequencies
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part I
An efficient approach to mine rare association rules using maximum items' support constraints
BNCOD'10 Proceedings of the 27th British national conference on Data Security and Security Data
Information Sciences: an International Journal
Mining association rules for the quality improvement of the production process
Expert Systems with Applications: An International Journal
An efficient tree-based algorithm for mining sequential patterns with multiple minimum supports
Journal of Systems and Software
Mining frequent patterns and association rules using similarities
Expert Systems with Applications: An International Journal
Fuzzy association rule mining approaches for enhancing prediction performance
Expert Systems with Applications: An International Journal
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Mining association rules with multiple minimum supports is an important generalization of the association-rule-mining problem, which was recently proposed by Liu et al. Instead of setting a single minimum support threshold for all items, they allow users to specify multiple minimum supports to reflect the natures of the items, and an Apriori-based algorithm, named MSapriori, is developed to mine all frequent itemsets. In this paper, we study the same problem but with two additional improvements. First, we propose a FP-tree-like structure, MIS-tree, to store the crucial information about frequent patterns. Accordingly, an efficient MIS-tree-based algorithm, called the CFP-growth algorithm, is developed for mining all frequent itemsets. Second, since each item can have its own minimum support, it is very difficult for users to set the appropriate thresholds for all items at a time. In practice, users need to tune items' supports and run the mining algorithm repeatedly until a satisfactory end is reached. To speed up this time-consuming tuning process, an efficient algorithm which can maintain the MIS-tree structure without rescanning database is proposed. Experiments on both synthetic and real-life datasets show that our algorithms are much more efficient and scalable than the previous algorithm.