Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Fast discovery of association rules
Advances in knowledge discovery and data mining
Discovering Patterns from Large and Dynamic Sequential Data
Journal of Intelligent Information Systems
Data mining, hypergraph transversals, and machine learning (extended abstract)
PODS '97 Proceedings of the sixteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Efficient enumeration of frequent sequences
Proceedings of the seventh international conference on Information and knowledge management
Incremental and interactive sequence mining
Proceedings of the eighth international conference on Information and knowledge management
FreeSpan: frequent pattern-projected sequential pattern mining
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
An efficient approach to discovering knowledge from large databases
DIS '96 Proceedings of the fourth international conference on on Parallel and distributed information systems
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
Using a Hash-Based Method with Transaction Trimming for Mining Association Rules
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
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
Proceedings of the 17th International Conference on Data Engineering
Mining Algorithms for Sequential Patterns in Parallel: Hash Based Approach
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining
A General Incremental Technique for Maintaining Discovered Association Rules
Proceedings of the Fifth International Conference on Database Systems for Advanced Applications (DASFAA)
Efficient Mining of Spatiotemporal Patterns
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
Efficient algorithms for incremental maintenance of closed sequential patterns in large databases
Data & Knowledge Engineering
Sequential association rules for forecasting failure patterns of aircrafts in Korean airforce
Expert Systems with Applications: An International Journal
IMCS: incremental mining of closed sequential patterns
APWeb/WAIM'07 Proceedings of the joint 9th Asia-Pacific web and 8th international conference on web-age information management conference on Advances in data and web management
A flexible and efficient sequential pattern mining algorithm
International Journal of Intelligent Information and Database Systems
Analysis on repeat-buying patterns
Knowledge-Based Systems
Efficient incremental mining of frequent sequence generators
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications - Volume Part I
Mining the change of customer behavior in fuzzy time-interval sequential patterns
Applied Soft Computing
Incremental mining of sequential patterns: Progress and challenges
Intelligent Data Analysis
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Current approaches for sequential pattern mining usually assume that the mining is performed in a static sequence database. However, databases are not static due to update so that the discovered patterns might become invalid and new patterns could be created. In addition to higher complexity, the maintenance of sequential patterns is more challenging than that of association rules owing to sequence merging. Sequence merging, which is unique in sequence databases, requires the appended new sequences to be merged with the existing ones if their customer ids are the same. Re-mining of the whole database appears to be inevitable since the information collected in previous discovery will be corrupted by sequence merging. Instead of re-mining, the proposed IncSP (Incremental Sequential Pattern Update) algorithm solves the maintenance problem through effective implicit merging and efficient separate counting over appended sequences. Patterns found previously are incrementally updated rather than re-mined from scratch. Moreover, the technique of early candidate pruning further speeds up the discovery of new patterns. Empirical evaluation using comprehensive synthetic data shows that IncSP is fast and scalable.