Experiments of the effectiveness of dataflow- and controlflow-based test adequacy criteria
ICSE '94 Proceedings of the 16th international conference on Software engineering
FreeSpan: frequent pattern-projected sequential pattern mining
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
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
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
Proceedings of the 17th International Conference on Data Engineering
The PSP Approach for Mining Sequential Patterns
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Managing Interesting Rules in Sequence Mining
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Mining All Non-derivable Frequent Itemsets
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
Sequential PAttern mining using a bitmap representation
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Top.K Frequent Closed Patterns without Minimum Support
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
BIDE: Efficient Mining of Frequent Closed Sequences
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Mining Non-Redundant Association Rules
Data Mining and Knowledge Discovery
Locating causes of program failures
Proceedings of the 27th international conference on Software engineering
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Proceedings of the 10th European software engineering conference held jointly with 13th ACM SIGSOFT international symposium on Foundations of software engineering
Perracotta: mining temporal API rules from imperfect traces
Proceedings of the 28th international conference on Software engineering
Mining progressive confident rules
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
SMArTIC: towards building an accurate, robust and scalable specification miner
Proceedings of the 14th ACM SIGSOFT international symposium on Foundations of software engineering
Applying sequential rules to protein localization prediction
Computers & Mathematics with Applications
Efficient mining of frequent sequence generators
Proceedings of the 17th international conference on World Wide Web
A machine learning approach for statistical software testing
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Efficient mining of recurrent rules from a sequence database
DASFAA'08 Proceedings of the 13th international conference on Database systems for advanced applications
A new hybrid method of generation of decision rules using the constructive induction mechanism
RSKT'10 Proceedings of the 5th international conference on Rough set and knowledge technology
Mining closed discriminative dyadic sequential patterns
Proceedings of the 14th International Conference on Extending Database Technology
RuleGrowth: mining sequential rules common to several sequences by pattern-growth
Proceedings of the 2011 ACM Symposium on Applied Computing
Reliable representations for association rules
Data & Knowledge Engineering
CMRules: Mining sequential rules common to several sequences
Knowledge-Based Systems
ICDM'11 Proceedings of the 11th international conference on Advances in data mining: applications and theoretical aspects
Extracting significant specifications from mining through mutation testing
ICFEM'11 Proceedings of the 13th international conference on Formal methods and software engineering
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part II
Mining sequential rules common to several sequences with the window size constraint
Canadian AI'12 Proceedings of the 25th Canadian conference on Advances in Artificial Intelligence
Mining association rules for the quality improvement of the production process
Expert Systems with Applications: An International Journal
Closeness Preference - A new interestingness measure for sequential rules mining
Knowledge-Based Systems
TNS: mining top-k non-redundant sequential rules
Proceedings of the 28th Annual ACM Symposium on Applied Computing
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A sequential rule expresses a relationship between two series of events happening one after another. Sequential rules are potentially useful for analyzing data in sequential format, ranging from purchase histories, network logs and program execution traces. In this work, we investigate and propose a syntactic characterization of a non-redundant set of sequential rules built upon past work on compact set of representative patterns. A rule is redundant if it can be inferred from another rule having the same support and confidence. When using the set of mined rules as a composite filter, replacing a full set of rules with a non-redundant subset of the rules does not impact the accuracy of the filter. We consider several rule sets based on composition of various types of pattern sets-generators, projected-database generators, closed patterns and projected-database closed patterns. We investigate the completeness and tightness of these rule sets. We characterize a tight and complete set of non-redundant rules by defining it based on the composition of two pattern sets. Furthermore, we propose a compressed set of non-redundant rules in a spirit similar to how closed patterns serve as a compressed representation of a full set of patterns. Lastly, we propose an algorithm to mine this compressed set of non-redundant rules. A performance study shows that the proposed algorithm significantly improves both the runtime and compactness of mined rules over mining a full set of sequential rules.