A sequential algorithm for training text classifiers
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Finding interesting rules from large sets of discovered association rules
CIKM '94 Proceedings of the third international conference on Information and knowledge management
Classification by feature partitioning
Machine Learning
Unexpectedness as a measure of interestingness in knowledge discovery
Decision Support Systems - Special issue on WITS '97
Analyzing the Subjective Interestingness of Association Rules
IEEE Intelligent Systems
Classification by Voting Feature Intervals
ECML '97 Proceedings of the 9th European Conference on Machine Learning
Interestingness of Discovered Association Rules in Terms of Neighborhood-Based Unexpectedness
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining
On Objective Measures of Rule Surprisingness
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Methods for cost-sensitive learning
Methods for cost-sensitive learning
A Framework for Evaluating Knowledge-Based Interestingness of Association Rules
Fuzzy Optimization and Decision Making
Evaluation of rule interestingness measures with a clinical dataset on hepatitis
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Proceedings of the 2005 ACM symposium on Applied computing
Text categorization using feature projections
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
A survey of interestingness measures for knowledge discovery
The Knowledge Engineering Review
Comparison between objective interestingness measures and real human interest in medical data mining
IEA/AIE'2004 Proceedings of the 17th international conference on Innovations in applied artificial intelligence
Journal of Artificial Intelligence Research
Post-analysis of learned rules
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
A nearest features classifier using a self-organizing map for memory base evaluation
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
Evaluating the correlation between objective rule interestingness measures and real human interest
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Investigation of rule interestingness in medical data mining
AM'03 Proceedings of the Second international conference on Active Mining
Discovering interesting association rules by clustering
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
A data analysis approach for evaluating the behavior of interestingness measures
DS'05 Proceedings of the 8th international conference on Discovery Science
Diagnosis of gastric carcinoma by classification on feature projections
Artificial Intelligence in Medicine
Interestingness measures for association rules based on statistical validity
Knowledge-Based Systems
Domain-driven KDD for mining functionally novel rules and linking disjoint medical hypotheses
Knowledge-Based Systems
Incremental learning of complete linear discriminant analysis for face recognition
Knowledge-Based Systems
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In a typical application of association rule learning from market basket data, a set of transactions for a fixed period of time is used as input to rule learning algorithms. For example, the well-known Apriori algorithm can be applied to learn a set of association rules from such a transaction set. However, learning association rules from a set of transactions is not a one time only process. For example, a market manager may perform the association rule learning process once every month over the set of transactions collected through the last month. For this reason, we will consider the problem where transaction sets are input to the system as a stream of packages. The sets of transactions may come in varying sizes and in varying periods. Once a set of transactions arrive, the association rule learning algorithm is executed on the last set of transactions, resulting in new association rules. Therefore, the set of association rules learned will accumulate and increase in number over time, making the mining of interesting ones out of this enlarging set of association rules impractical for human experts. We refer to this sequence of rules as ''association rule set stream'' or ''streaming association rules'' and the main motivation behind this research is to develop a technique to overcome the interesting rule selection problem. A successful association rule mining system should select and present only the interesting rules to the domain experts. However, definition of interestingness of association rules on a given domain usually differs from one expert to another and also over time for a given expert. This paper proposes a post-processing method to learn a subjective model for the interestingness concept description of the streaming association rules. The uniqueness of the proposed method is its ability to formulate the interestingness issue of association rules as a benefit-maximizing classification problem and obtain a different interestingness model for each user. In this new classification scheme, the determining features are the selective objective interestingness factors related to the interestingness of the association rules, and the target feature is the interestingness label of those rules. The proposed method works incrementally and employs user interactivity at a certain level. It is evaluated on a real market dataset. The results show that the model can successfully select the interesting ones.