Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
A multilevel approach to intelligent information filtering: model, system, and evaluation
ACM Transactions on Information Systems (TOIS)
Mining Text Using Keyword Distributions
Journal of Intelligent Information Systems
Mining the World Wide Web: an information search approach
Mining the World Wide Web: an information search approach
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Multipass algorithms for mining association rules in text databases
Knowledge and Information Systems
Information Retrieval: Algorithms and Heuristics
Information Retrieval: Algorithms and Heuristics
Modern Information Retrieval
Mining Multiple-Level Association Rules in Large Databases
IEEE Transactions on Knowledge and Data Engineering
Web mining for web personalization
ACM Transactions on Internet Technology (TOIT)
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
In Pursuit of Patterns in Data Reasoning from Data The Rough Set Way
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
PEBL: positive example based learning for Web page classification using SVM
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Text Document Categorization by Term Association
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Interpretations of Association Rules by Granular Computing
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
The Rough Set Approach to Association Rule Mining
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Building Text Classifiers Using Positive and Unlabeled Examples
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
TSP: Mining Top-K Closed Sequential Patterns
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Automatic Pattern-Taxonomy Extraction for Web Mining
WI '04 Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence
Capturing Evolving Patterns for Ontology-based Web Mining
WI '04 Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence
Data Mining and Knowledge Discovery
Mining Ontology for Automatically Acquiring Web User Information Needs
IEEE Transactions on Knowledge and Data Engineering
Learning to classify texts using positive and unlabeled data
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Flow graphs and decision algorithms
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
Ontology based web mining for information gathering
WImBI'06 Proceedings of the 1st WICI international conference on Web intelligence meets brain informatics
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It is a big challenge to guarantee the quality of association rules in some application areas (e.g., in Web information gathering) since duplications and ambiguities of data values (e.g., terms). Rough set based decision tables could be efficient tools for solving this challenge. This paper first illustrates the relationship between decision tables and association mining. It proves that a decision rule is a kind of closed pattern. It also presents an alternative concept of rough association rules to improve the quality of discovered knowledge in this area. The premise of a rough association rule consists of a set of terms (items) and a weight distribution of terms (items). The distinct advantage of rough association rules is that they contain more specific information than normal association rules. This paper also conducts some experiments to compare the proposed method with association rule mining and decision tables; and the experimental results verify that the proposed approach is promising.