Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Frequent Itemsets without Support Threshold: With and without Item Constraints
IEEE Transactions on Knowledge and Data Engineering
Mining top-K high utility itemsets
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
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Data mining is the discovery of interesting and hidden patterns from a large amount of collected data. Applications can be found in many organisations with large databases, for many different purposes such as customer relationships, marketing, planning, scientific discovery, and other data analysis. In this paper, the problem of mining N-most interesting itemsets is addressed. We make use of the techniques of COFI-tree in order to tackle the problem. In our experiments, we find that our proposed algorithm based on COFI-tree performs faster than the previous approach BOMO based on the FP-tree.