Integrating association rule mining with relational database systems: alternatives and implications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
TBAR: An efficient method for association rule mining in relational databases
Data & Knowledge Engineering
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
Using a Hash-Based Method with Transaction Trimming for Mining Association Rules
IEEE Transactions on Knowledge and Data Engineering
Set-Oriented Mining for Association Rules in Relational Databases
ICDE '95 Proceedings of the Eleventh 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
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In this paper, we propose the SETM*-MaxK algorithm to find the largest itemset based on a high-level set-based approach, where a large itemset is a set of items appearing in a sufficient number of transactions. The advantage of the set-based approach, like the SETM algorithm, is simple and stable over the range of parameter values. In the SETM*-MaxK algorithm, we efficiently find the Lk based on Lw, where Lk denotes the set of large k-itemsets with minimum support, Lk 驴 0, Lk+1 = 0 and w = 2驴log2k驴-1, instead of step by step. From our simulation, we show that the proposed SETM*-MaxK algorithm requires shorter time to achieve its goal than the SETM algorithm.