Turbo-charging vertical mining of large databases
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
The application of association rule mining to remotely sensed data
SAC '00 Proceedings of the 2000 ACM symposium on Applied computing - Volume 1
Prediction of Web Page Accesses by Proxy Server Log
World Wide Web
Scalable Algorithms for Association Mining
IEEE Transactions on Knowledge and Data Engineering
A Graph-Based Approach for Discovering Various Types of Association Rules
IEEE Transactions on Knowledge and Data Engineering
An Efficient Approach to Discovering Sequential Patterns in Large Databases
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Efficient Graph-Based Algorithm for Discovering and Maintaining Knowledge in Large Databases
PAKDD '99 Proceedings of the Third Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data Mining
Mining Interesting Association Rules: A Data Mining Language
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
An Effective Boolean Algorithm for Mining Association Rules in Large Databases
DASFAA '99 Proceedings of the Sixth International Conference on Database Systems for Advanced Applications
An Efficient Approach for Incremental Association Rule Mining
PAKDD '99 Proceedings of the Third Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data Mining
Information Systems - Databases: Creation, management and utilization
ACM Computing Surveys (CSUR)
Automated ontology construction for unstructured text documents
Data & Knowledge Engineering
Discovering gene-gene relations from sequential sentence patterns in biomedical literature
Expert Systems with Applications: An International Journal
Building intrusion pattern miner for Snort network intrusion detection system
Journal of Systems and Software
Discovering frequent itemsets by support approximation and itemset clustering
Data & Knowledge Engineering
Maintenance of informative ruler sets for predictions
Intelligent Data Analysis
RMAIN: Association rules maintenance without reruns through data
Information Sciences: an International Journal
Mining Frequent Purchase Behavior Patterns for Commercial Websites
ICCCI '09 Proceedings of the 1st International Conference on Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems
Mining Indirect Association Rules for Web Recommendation
International Journal of Applied Mathematics and Computer Science
Generalized association rule mining using an efficient data structure
Expert Systems with Applications: An International Journal
An improved association rules mining method
Expert Systems with Applications: An International Journal
Adjustment of indirect association rules for the web
SOFSEM'05 Proceedings of the 31st international conference on Theory and Practice of Computer Science
GMA: an approach for association rules mining on medical images
ICIC'12 Proceedings of the 8th international conference on Intelligent Computing Theories and Applications
Mining multidimensional frequent patterns from relational database
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part I
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In this paper, we study two problems: mining association rules and mining sequential patterns in a large database of customer transactions. The problem of mining association rules focuses on discovering large itemsets where a large itemset is a group of items which appear together in a sufficient number of transactions: while the problem of mining sequential patterns focuses on discovering large sequences where a large sequence is an ordered list of sets of items which appear in a sufficient number of transactions. We present efficient graph-based algorithms to solve these problems. The algorithms construct an association graph to indicate the associations between items and then traverse the graph to generate large itemsets and large sequences, respectively. Our algorithms need to scan the database only once. Empirical evaluations show that our algorithms outperform other algorithms which need to make multiple passes over the database.