TREC-2 Proceedings of the second conference on Text retrieval conference
Clumping properties of content-bearing words
Journal of the American Society for Information Science
Mining association rules with multiple minimum supports
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient mining of weighted association rules (WAR)
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Multipass algorithms for mining association rules in text databases
Knowledge and Information Systems
Finding Interesting Associations without Support Pruning
IEEE Transactions on Knowledge and Data Engineering
Text Document Categorization by Term Association
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Mining Association Rules with Weighted Items
IDEAS '98 Proceedings of the 1998 International Symposium on Database Engineering & Applications
Visualizing Association Rules for Text Mining
INFOVIS '99 Proceedings of the 1999 IEEE Symposium on Information Visualization
Building Text Classifiers Using Positive and Unlabeled Examples
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
PEBL: Web Page Classification without Negative Examples
IEEE Transactions on Knowledge and Data Engineering
Weighted Association Rule Mining using weighted support and significance framework
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Automatic Pattern-Taxonomy Extraction for Web Mining
WI '04 Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Visual text mining using association rules
Computers and Graphics
Redundant association rules reduction techniques
International Journal of Business Intelligence and Data Mining
ODAM: An Optimized Distributed Association Rule Mining Algorithm
IEEE Distributed Systems Online
Mining Both Positive and Negative Weighted Association Rules with Multiple Minimum Supports
CSSE '08 Proceedings of the 2008 International Conference on Computer Science and Software Engineering - Volume 04
Improvement of Text Feature Selection Method Based on TFIDF
FITME '08 Proceedings of the 2008 International Seminar on Future Information Technology and Management Engineering
Mining Weighted Negative Association Rules Based on Correlation from Infrequent Items
ICACC '09 Proceedings of the 2009 International Conference on Advanced Computer Control
Finding sporadic rules using apriori-inverse
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Incorporating pageview weight into an association-rule-based web recommendation system
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Valency based weighted association rule mining
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Mining Frequent Generalized Patterns for Web Personalization in the Presence of Taxonomies
International Journal of Data Warehousing and Mining
Weighted association rule mining via a graph based connectivity model
Information Sciences: an International Journal
A data mining approach to knowledge discovery from multidimensional cube structures
Knowledge-Based Systems
User Behaviour Pattern Mining from Weblog
International Journal of Data Warehousing and Mining
FAR-miner: a fast and efficient algorithm for fuzzy association rule mining
International Journal of Business Intelligence and Data Mining
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Association rule mining discovers relationships among items in a transactional database. Most approaches assume that all items within a dataset have a uniform distribution with respect to support. However, this is not always the case, and weighted association rule mining WARM was introduced to provide importance to individual items. Previous approaches to the weighted association rule mining problem require users to assign weights to items. In certain cases, it is difficult to provide weights to all items within a dataset. In this paper, the authors propose a method that is based on a novel Valency model that automatically infers item weights based on interactions between items. The authors experiment shows that the weighting scheme results in rules that better capture the natural variation that occurs in a dataset when compared with a miner that does not employ a weighting scheme. The authors applied the model in a real world application to mine text from a given collection of documents. The use of item weighting enabled the authors to attach more importance to terms that are distinctive. The results demonstrate that keyword discrimination via item weighting leads to informative rules.