ACM Transactions on Database Systems (TODS)
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Recent directions in netlist partitioning: a survey
Integration, the VLSI Journal
A database perspective on knowledge discovery
Communications of the ACM
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Using Condensed Representations for Interactive Association Rule Mining
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Incremental Refinement of Mining Queries
DaWaK '99 Proceedings of the First International Conference on Data Warehousing and Knowledge Discovery
Optimization of a language for data mining
Proceedings of the 2003 ACM symposium on Applied computing
Optimizing a sequence of frequent pattern queries
DaWaK'05 Proceedings of the 7th international conference on Data Warehousing and Knowledge Discovery
On multiple query optimization in data mining
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
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We consider the problem of concurrent execution of multiple frequent itemset queries. If such data mining queries operate on overlapping parts of the database, then their overall I/O cost can be reduced by integrating their dataset scans. The integration requires that data structures of many data mining queries are present in memory at the same time. If the memory size is not sufficient to hold all the data mining queries, then the queries must be scheduled into multiple phases of loading and processing. Since finding the optimal assignment of queries to phases is infeasible for large batches of queries due to the size of the search space, heuristic algorithms have to be applied. In this paper we formulate the problem of assigning the queries to phases as a particular case of hypergraph partitioning. To solve the problem, we propose and experimentally evaluate two greedy optimization algorithms.