Finding interesting rules from large sets of discovered association rules
CIKM '94 Proceedings of the third international conference on Information and knowledge management
An effective hash-based algorithm for mining association rules
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Exploratory mining and pruning optimizations of constrained associations rules
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Efficiently mining long patterns from databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Integrating association rule mining with relational database systems: alternatives and implications
SIGMOD '98 Proceedings of the 1998 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
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
A tree projection algorithm for generation of frequent item sets
Journal of Parallel and Distributed Computing - Special issue on high-performance data mining
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
A New Approach to Online Generation of Association Rules
IEEE Transactions on Knowledge and Data Engineering
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Scalable Techniques for Mining Causal Structures
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
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
Efficient Mining of Partial Periodic Patterns in Time Series Database
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
An efficient and flexible algorithm for online mining of large itemsets
Information Processing Letters
Research issues in data stream association rule mining
ACM SIGMOD Record
Online mining of fuzzy multidimensional weighted association rules
Applied Intelligence
An efficient and flexible algorithm for online mining of large itemsets
Information Processing Letters
Integrating fuzziness with OLAP association rules mining
MLDM'03 Proceedings of the 3rd international conference on Machine learning and data mining in pattern recognition
A sampling-based method for mining frequent patterns from databases
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II
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Mining frequent patterns in transaction databases has been a popular subject in data mining research. Common activities include finding patterns in database transactions, times-series, and exceptions. The Apriori algorithm is a widely accepted method of generating frequent patterns. The algorithm can require many scans of the database and can seriously tax resources. New methods of finding association rules, such as the Frequent Pattern Tree (FP-Tree) have improved performance, but still have problems when new data becomes available and require two scans of the database.This paper proposes a new method, which requires only one scan of the database and supports update of patterns when new data becomes available. We design a new structure called Pattern Repository (PR), which stores all of the relevant information in a highly compact form and allows direct derivation of the FP-Tree and association rules quickly with a minimum of resources. In addition, it supports run-time generation of association rules by considering only those patterns that meet on-line data requirements.