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
A database perspective on knowledge discovery
Communications of the ACM
Using a knowledge cache for interactive discovery of association rules
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Materialized Data Mining Views
PKDD '00 Proceedings of the 4th 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
Improving the efficiency of inductive logic programming through the use of query packs
Journal of Artificial Intelligence Research
Region clustering based evaluation of multiple top-N selection queries
Data & Knowledge Engineering
Three strategies for concurrent processing of frequent itemset queries using FP-growth
KDID'06 Proceedings of the 5th international conference on Knowledge discovery in inductive databases
A greedy approach to concurrent processing of frequent itemset queries
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
Partition-Based approach to processing batches of frequent itemset queries
FQAS'06 Proceedings of the 7th international conference on Flexible Query Answering Systems
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Traditional multiple query optimization methods focus on identifying common subexpressions in sets of relational queries and on constructing their global execution plans. In this paper we consider the problem of optimizing sets of data mining queries submitted to a Knowledge Discovery Management System. We describe the problem of data mining query scheduling and we introduce a new algorithm called CCAgglomerative to schedule data mining queries for frequent itemset discovery.