Finding interesting rules from large sets of discovered association rules
CIKM '94 Proceedings of the third international conference on Information and knowledge management
Information Processing and Management: an International Journal - Special issue on history of information science
Literature-based discovery by lexical statistics
Journal of the American Society for Information Science
Journal of the American Society for Information Science and Technology
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Rating the Interest of Rules Induced from Data and within Texts
DEXA '01 Proceedings of the 12th International Workshop on Database and Expert Systems Applications
LitLinker: capturing connections across the biomedical literature
Proceedings of the 2nd international conference on Knowledge capture
Text mining: generating hypotheses from MEDLINE
Journal of the American Society for Information Science and Technology
A Data Mining Approach to PubMed Query Refinement
DEXA '04 Proceedings of the Database and Expert Systems Applications, 15th International Workshop
Natural Language Engineering
Detecting privacy leaks using corpus-based association rules
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Bioinformatics integration framework for metabolic pathway data-mining
IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems
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The discovery of new and potentially meaningful relationships between concepts in the biomedical literature has attracted the attention of a lot of researchers in text mining. The main motivation is found in the increasing availability of the biomedical literature which makes it difficult for researchers in biomedicine to keep up with research progresses without the help of automatic knowledge discovery techniques. More than 14 million abstracts of this literature are contained in the Medline collection and are available online. In this paper we present the application of an association rule mining method to Medline abstracts in order to detect associations between concepts as indication of the existence of a biomedical relation among them. The discovery process fully exploits the MeSH (Medical Subject Headings) taxonomy, that is, a set of hierarchically related biomedical terms which permits to express associations at different levels of abstraction (generalized association rules). We report experimental results on a collection of abstracts obtained by querying Medline on a specific disease and we show the effectiveness of some filtering and browsing techniques designed to manage the huge amount of generalized associations that may be generated on real data.