Natural language understanding (2nd ed.)
Natural language understanding (2nd ed.)
A knowledge-based method for temporal abstraction of clinical data
A knowledge-based method for temporal abstraction of clinical data
Exploratory mining and pruning optimizations of constrained associations rules
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
BIOKDD01: workshop on Data Mining in Bioinformatics
ACM SIGKDD Explorations Newsletter
Data Mining for Scientific and Engineering Applications
Data Mining for Scientific and Engineering Applications
Introduction To Automata Theory, Languages, And Computation
Introduction To Automata Theory, Languages, And Computation
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
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
On the Discovery of Interesting Patterns in Association Rules
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
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
Constraint relaxations for discovering unknown sequential patterns
KDID'04 Proceedings of the Third international conference on Knowledge Discovery in Inductive Databases
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One of the main unresolved problems in data mining is related with the treatment of data that is inherently sequential. Algorithms for the inference of association rules that manipulate sequential data have been proposed and used to some extent but are ineffective, in some cases, because too many candidate rules are extracted and filtering the relevant ones is difficult and inefficient. In this work, we present a method and algorithm for the inference of sequential association rules that uses context-free grammars to guide the discovery process, in order to filter, in an efficient and effective way, the associations discovered by the algorithm.