Fast Algorithms for Frequent Itemset Mining Using FP-Trees
IEEE Transactions on Knowledge and Data Engineering
Out-of-core frequent pattern mining on a commodity PC
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
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Knowledge and Information Systems
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Proceedings of the 2010 ACM Symposium on Applied Computing
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IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
Shaping SQL-Based frequent pattern mining algorithms
KDID'05 Proceedings of the 4th international conference on Knowledge Discovery in Inductive Databases
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ACACOS'12 Proceedings of the 11th WSEAS international conference on Applied Computer and Applied Computational Science
Stream mining of frequent sets with limited memory
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Mining frequent itemsets from sparse data streams in limited memory environments
WAIM'13 Proceedings of the 14th international conference on Web-Age Information Management
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Mining frequent itemsets is at the core of mining association rules, and is by now quite well understood algorithmically for main memory databases. In this paper, we investigate approaches to mining frequent itemsets when the database or the data structures used in the mining are too large to fit in main memory. Experimental results show that our techniques reduce the required disk accesses by orders of magnitude, and enable truly scalable data mining.