dc.contributor.author | Huang, Yo-Ping | |
dc.contributor.author | Vu, Thi Thanh Hoa | |
dc.contributor.author | Jau, Jung-Shian | |
dc.contributor.author | Sandnes, Frode Eika | |
dc.date.accessioned | 2010-11-08T12:10:05Z | |
dc.date.available | 2010-11-08T12:10:05Z | |
dc.date.issued | 2010-09 | |
dc.identifier.citation | Huang, Y., Vu, T., Jau, J. & Sandnes, F.E. (2010). A Fuzzy ART2 Model for Finding Association Rules in Medical Data. In: P. Sobrevilla (Ed.), IEEE World Congress on Computational Intelligence. Piscataway, N.J. : Institute of Electrical and Electronics Engineers | en_US |
dc.identifier.isbn | 978-1-4244-8126-2 | |
dc.identifier.issn | 1098-7584 | |
dc.identifier.uri | https://hdl.handle.net/10642/499 | |
dc.description.abstract | This paper describes a model that discovers
association rules from a medical database to help doctors treat
and diagnose a group of patients who show similar prehistoric
medical symptoms. The proposed data mining procedure
consists of two modules. The first is a clustering module that is
based on a neural network, Adaptive Resonance Theory 2
(ART2), which performs affinity grouping tasks on a large
amount of medical records. The other module employs fuzzy set
theory to extract fuzzy association rules for each homogeneous
cluster of data records. In addition, an example is given to
illustrate this model. Simulation results show that the proposed
algorithm can be used to obtain the desired results with a
reduced processing time. | en_US |
dc.description.sponsorship | This work was supported by National
Science Council, Taiwan under Grant NSC97-2221-E-027-034-MY3. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | IEEE Computer Society | en_US |
dc.subject | Data mining | en_US |
dc.subject | Fuzzy set theory | en_US |
dc.subject | Pattern clustering | en_US |
dc.subject | Fuzzy association | en_US |
dc.subject | VDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550::Datateknologi: 551 | en_US |
dc.title | A Fuzzy ART2 Model for Finding Association Rules in Medical Data | en_US |
dc.type | Chapter | en_US |
dc.type | Peer reviewed | en_US |
dc.identifier.doi | http://dx.doi.org/10.1109/FUZZY.2010.5584780 | |