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
Exploratory mining and pruning optimizations of constrained associations rules
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Mining frequent patterns by pattern-growth: methodology and implications
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
Real world performance of association rule algorithms
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Mining Frequent Item Sets with Convertible Constraints
Proceedings of the 17th International Conference on Data Engineering
SPIRIT: Sequential Pattern Mining with Regular Expression Constraints
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Discovery of maximum length frequent itemsets
Information Sciences: an International Journal
An improved data mining approach using predictive itemsets
Expert Systems with Applications: An International Journal
Dataset filtering based association rule updating in small-sized temporal databases
ICCSA'05 Proceedings of the 2005 international conference on Computational Science and Its Applications - Volume Part IV
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Many data mining techniques consist in discovering patterns frequently occurring in the source dataset. Typically, the goal is to discover all the patterns whose frequency in the dataset exceeds a userspecified threshold. However, very often users want to restrict the set of patterns to be discovered by adding extra constraints on the structure of patterns. Data mining systems should be able to exploit such constraints to speed-up the mining process. In this paper, we focus on improving the efficiency of constraint-based frequent pattern mining by using dataset filtering techniques. Dataset filtering conceptually transforms a given data mining task into an equivalent one operating on a smaller dataset. We present transformation rules for various classes of patterns: itemsets, association rules, and sequential patterns, and discuss implementation issues regarding integration of dataset filtering with well-known pattern discovery algorithms.