Fast subsequence matching in time-series databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Efficient mining of emerging patterns: discovering trends and differences
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
FreeSpan: frequent pattern-projected sequential pattern mining
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Data mining: concepts and techniques
Data mining: concepts and techniques
SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
Mining web logs to improve website organization
Proceedings of the 10th international conference on World Wide Web
Multi-dimensional sequential pattern mining
Proceedings of the tenth international conference on Information and knowledge management
Mining long sequential patterns in a noisy environment
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Constrained frequent pattern mining: a pattern-growth view
ACM SIGKDD Explorations Newsletter
Knowledge Discovery in Databases
Knowledge Discovery in Databases
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
Mining hybrid sequential patterns and sequential rules
Information Systems
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
Efficient Data Mining for Path Traversal Patterns
IEEE Transactions on Knowledge and Data Engineering
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Mining Partially Periodic Event Patterns with Unknown Periods
Proceedings of the 17th International Conference on Data Engineering
Analyzing the Interestingness of Association Rules from the Temporal Dimension
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
SPIRIT: Sequential Pattern Mining with Regular Expression Constraints
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
On Computing Condensed Frequent Pattern Bases
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Mining General Temporal Association Rules for Items with Different Exhibition Periods
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Efficient Mining of Partial Periodic Patterns in Time Series Database
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Progressive Partition Miner: An Efficient Algorithm for Mining General Temporal Association Rules
IEEE Transactions on Knowledge and Data Engineering
Discovery of Fuzzy Sequential Patterns for Fuzzy Partitions in Quantitative Attributes
AICCSA '01 Proceedings of the ACS/IEEE International Conference on Computer Systems and Applications
Discovering Calendar-Based Temporal Association Rules
TIME '01 Proceedings of the Eighth International Symposium on Temporal Representation and Reasoning (TIME'01)
Efficient closed pattern mining in the presence of tough block constraints
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach
IEEE Transactions on Knowledge and Data Engineering
Adequacy of training data for evolutionary mining of trading rules
Decision Support Systems - Special issue: Data mining for financial decision making
Mining condensed frequent-pattern bases
Knowledge and Information Systems
A bootstrap evaluation of the effect of data splitting on financial time series
IEEE Transactions on Neural Networks
A change detection method for sequential patterns
Decision Support Systems
Mining frequent trajectory patterns in spatial-temporal databases
Information Sciences: an International Journal
Handling sequential pattern decay: Developing a two-stage collaborative recommender system
Electronic Commerce Research and Applications
Sequential pattern mining algorithm for automotive warranty data
Computers and Industrial Engineering
Discovering hybrid temporal patterns from sequences consisting of point- and interval-based events
Data & Knowledge Engineering
Mining sequential patterns in the B2B environment
Journal of Information Science
An approach to discovering multi-temporal patterns and its application to financial databases
Information Sciences: an International Journal
Analysis on repeat-buying patterns
Knowledge-Based Systems
New approach for the sequential pattern mining of high-dimensional sequence databases
Decision Support Systems
Knowledge discovery of weighted RFM sequential patterns from customer sequence databases
Journal of Systems and Software
A data mining approach to discovering reliable sequential patterns
Journal of Systems and Software
Incorporating frequency, recency and profit in sequential pattern based recommender systems
Intelligent Data Analysis
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Sequential pattern mining is an important data-mining method for determining time-related behavior in sequence databases. The information obtained from sequential pattern mining can be used in marketing, medical records, sales analysis, and so on. Existing methods only focus on the concept of frequency because of the assumption that sequences' behaviors do not change over time. The environment from which the data is generated is often dynamic, however, so the sequences' behaviors may change over time. To adapt the discovered patterns to these changes, two new concepts, recency and compactness, are incorporated into traditional sequential pattern mining. The concept of recency causes patterns to quickly adapt to the latest behaviors in sequence databases, while the concept of compactness ensures reasonable time spans for the discovered patterns. We named the new patterns CFR-patterns because three concepts (compactness, frequency, and recency) are simultaneously considered. An efficient method is presented to find CFR-patterns. Empirical evaluation shows that the proposed methods are computationally efficient and that they are more advantageous than traditional methods when sequences' behaviors change over time.