Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Discovering Patterns from Large and Dynamic Sequential Data
Journal of Intelligent Information Systems
Efficient mining of association rules using closed itemset lattices
Information Systems
Incremental and interactive sequence mining
Proceedings of the eighth international conference on Information and knowledge management
Quantifying the utility of the past in mining large databases
Information Systems
Is Sampling Useful in Data Mining? A Case in the Maintenance of Discovered Association Rules
Data Mining and Knowledge Discovery
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
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
An Efficient Algorithm for Mining Association Rules in Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Sampling Large Databases for Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
A General Incremental Technique for Maintaining Discovered Association Rules
Proceedings of the Fifth International Conference on Database Systems for Advanced Applications (DASFAA)
An Adaptive Algorithm for Incremental Mining of Association Rules
DEXA '98 Proceedings of the 9th International Workshop on Database and Expert Systems Applications
Scalable Data Mining for Rules
Scalable Data Mining for Rules
IncSpan: incremental mining of sequential patterns in large database
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
A fuzzy data mining algorithm for incremental mining of quantitative sequential patterns
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Sequential patterns for text categorization
Intelligent Data Analysis
Mining frequent tree-like patterns in large datasets
Data & Knowledge Engineering
Privacy preserving data mining of sequential patterns for network traffic data
Information Sciences: an International Journal
Efficient algorithms for incremental utility mining
Proceedings of the 2nd international conference on Ubiquitous information management and communication
Intelligent Data Analysis - Knowlegde Discovery from Data Streams
An efficient algorithm for mining closed inter-transaction itemsets
Data & Knowledge Engineering
Discovering Novel Multistage Attack Strategies
ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
Mining Sequential Patterns with Negative Conclusions
DaWaK '08 Proceedings of the 10th international conference on Data Warehousing and Knowledge Discovery
Efficient algorithms for incremental maintenance of closed sequential patterns in large databases
Data & Knowledge Engineering
A change detection method for sequential patterns
Decision Support Systems
Mining sequential patterns across multiple sequence databases
Data & Knowledge Engineering
Mining closed patterns in multi-sequence time-series databases
Data & Knowledge Engineering
Data & Knowledge Engineering
Discovering hybrid temporal patterns from sequences consisting of point- and interval-based events
Data & Knowledge Engineering
Incremental mining of sequential patterns using prefix tree
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
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
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
Improvements of incspan: incremental mining of sequential patterns in large database
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Distributed methodology of cantree construction
MIWAI'11 Proceedings of the 5th international conference on Multi-Disciplinary Trends in Artificial Intelligence
Discovering forward sequences from temporal data
Knowledge-Based Systems
User Behaviour Pattern Mining from Weblog
International Journal of Data Warehousing and Mining
CSSF-trie structure to mine constraint sequential patterns from progressive database
International Journal of Knowledge Engineering and Data Mining
Incremental mining of sequential patterns: Progress and challenges
Intelligent Data Analysis
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In this paper, we consider the problem of the incremental mining of sequential patterns when new transactions or new customers are added to an original database. We present a new algorithm for mining frequent sequences that uses information collected during an earlier mining process to cut down the cost of finding new sequential patterns in the updated database. Our test shows that the algorithm performs significantly faster than the naive approach of mining the whole updated database from scratch. The difference is so pronounced that this algorithm could also be useful for mining sequential patterns, since in many cases it is faster to apply our algorithm than to mine sequential patterns using a standard algorithm, by breaking down the database into an original database plus an increment.