Proverbs for programming in Pascal
Proverbs for programming in Pascal
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Finding interesting rules from large sets of discovered association rules
CIKM '94 Proceedings of the third international conference on Information and knowledge management
Online association rule mining
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Mining the most interesting rules
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Borders: An Efficient Algorithm for Association Generation in Dynamic Databases
Journal of Intelligent Information Systems
New algorithms for efficient mining of association rules
Information Sciences: an International Journal
An efficient approach to discovering knowledge from large databases
DIS '96 Proceedings of the fourth international conference on on Parallel and distributed information systems
Models and issues in data stream systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Is Sampling Useful in Data Mining? A Case in the Maintenance of Discovered Association Rules
Data Mining and Knowledge Discovery
Constraint-Based Rule Mining in Large, Dense Databases
Data Mining and Knowledge Discovery
Efficient Adaptive-Support Association Rule Mining for Recommender Systems
Data Mining and Knowledge Discovery
Parallel and Distributed Association Mining: A Survey
IEEE Concurrency
Scalable Algorithms for Association Mining
IEEE Transactions on Knowledge and Data Engineering
DEMON: Mining and Monitoring Evolving Data
IEEE Transactions on Knowledge and Data Engineering
A New Approach to Online Generation of Association Rules
IEEE Transactions on Knowledge and Data Engineering
Mining Optimized Association Rules with Categorical and Numeric Attributes
IEEE Transactions on Knowledge and Data Engineering
Simple association rules (SAR) and the SAR-based rule discovery
Computers and Industrial Engineering
A Statistical Theory for Quantitative Association Rules
Journal of Intelligent Information Systems
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th 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)
Mining Incremental Association Rules with Generalized FP-Tree
AI '02 Proceedings of the 15th Conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
Progressive Partition Miner: An Efficient Algorithm for Mining General Temporal Association Rules
IEEE Transactions on Knowledge and Data Engineering
Sequential Association Rule Mining with Time Lags
Journal of Intelligent Information Systems
Dataless Transitions Between Concise Representations of Frequent Patterns
Journal of Intelligent Information Systems
Tree Structures for Mining Association Rules
Data Mining and Knowledge Discovery
Finding recent frequent itemsets adaptively over online data streams
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Informative Rule Set for Prediction
Journal of Intelligent Information Systems
Inducing Multi-Level Association Rules from Multiple Relations
Machine Learning
Memory-adative association rules mining
Information Systems - Databases: Creation, management and utilization
Generating a Condensed Representation for Association Rules
Journal of Intelligent Information Systems
A high-performance distributed algorithm for mining association rules
Knowledge and Information Systems
Demand-driven frequent itemset mining using pattern structures
Knowledge and Information Systems
APS: Agent's LearningWith Imperfect Recall
ISDA '05 Proceedings of the 5th International Conference on Intelligent Systems Design and Applications
EDUA: An efficient algorithm for dynamic database mining
Information Sciences: an International Journal
Genetic algorithm based framework for mining fuzzy association rules
Fuzzy Sets and Systems
Mining association rules with multiple minimum supports using maximum constraints
International Journal of Approximate Reasoning
CBAR: an efficient method for mining association rules
Knowledge-Based Systems
Flexible online association rule mining based on multidimensional pattern relations
Information Sciences: an International Journal
Discovery of fuzzy temporal association rules
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Toward boosting distributed association rule mining by data de-clustering
Information Sciences: an International Journal
Measures for comparing association rule sets
ICAISC'10 Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part I
From data to global generalized knowledge
Decision Support Systems
A formal model for mining fuzzy rules using the RL representation theory
Information Sciences: an International Journal
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Association rules are well recognised as a data mining tool for analysis of transactional data, currently going far beyond the early basket-based applications. A wide spectrum of methods for mining associations have been proposed up to date, including batch and incremental approaches. Most of the accurate incremental methods minimise, but do not completely eliminate reruns through processed data. In this paper we propose a new approximate algorithm RMAIN for incremental maintenance of association rules, which works repeatedly on subsequent portions of new transactions. After a portion has been analysed, the new rules are combined with the old ones, so that no reruns through the processed transactions are performed in the future. The resulting set of rules is kept similar to the one that would be achieved in a batch manner. Unlike other incremental methods, RMAIN is fully separated from a rule mining algorithm and this independence makes it highly general and flexible. Moreover, it operates on rules in their final form, ready for decision support, and not on intermediate representation (frequent itemsets), which requires further processing. These features make the RMAIN algorithm well suited for rule maintenance within knowledge bases of autonomous systems with strongly bounded resources and time for decision making. We evaluated the algorithm on synthetic and real datasets, achieving promising results with respect to either performance or quality of output rules.