A database perspective on knowledge discovery
Communications of the ACM
Exploratory mining and pruning optimizations of constrained associations rules
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Optimization of constrained frequent set queries with 2-variable constraints
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Can we push more constraints into frequent pattern mining?
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Levelwise Search and Borders of Theories in KnowledgeDiscovery
Data Mining and Knowledge Discovery
An Extension to SQL for Mining Association Rules
Data Mining and Knowledge Discovery
On the Complexity of Mining Quantitative Association Rules
Data Mining and Knowledge Discovery
Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Mining Frequent Item Sets with Convertible Constraints
Proceedings of the 17th 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
A New SQL-like Operator for Mining Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Constraint-Based Discovery and Inductive Queries: Application to Association Rule Mining
Proceedings of the ESF Exploratory Workshop on Pattern Detection and Discovery
Optimization of association rule mining queries
Intelligent Data Analysis
Answering constraint-based mining queries on itemsets using previous materialized results
Journal of Intelligent Information Systems
Efficient online mining of large databases
International Journal of Business Information Systems
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
Inductive databases and constraint-based data mining
ICFCA'11 Proceedings of the 9th international conference on Formal concept analysis
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
A Framework for Synthesizing Arbitrary Boolean Queries Induced by Frequent Itemsets
International Journal of Knowledge-Based Organizations
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Constraint-based mining has attracted in recent years the interest of the data mining research community because it increases the relevance of the result set, reduces its volume and the amount of workload. However, constrained-based mining will be completely feasible only when efficient optimizers for mining languages will be available.This paper is a first step towards the construction of optimizers for a constraint-based mining language. It provides the guidelines for the comparison of classes of statements by means of the relationships existing between their result sets. Furthermore it identifies as useful information to the optimization the presence of unique constraints and functional dependencies in the schema of the database. We show the practical implications of the discussed principles with a set of algorithms designed for a specific mining language. These algorithms use also a new designed index, called mining index that allows to reduce the portion of the database to be read in response to some classes of queries. In these cases the workload of the mining engine is greatly reduced or completely avoided in a significant subset of the cases.