A Fuzzy ART2 Model for Finding Association Rules in Medical Data

被引：0

作者：

Huang, Yo-Ping ^{[1
]}

Vu Thi Thanh Hoa ^{[1
]}

Jau, Jung-Shian ^{[1
]}

Sandnes, Frode Eika ^{[2
]}

机构：

[1] Natl Taipei Univ Technol, Dept Elect Engn, Taipei 10608, Taiwan

[2] Oslo Univ Coll, Fac Engn, Oslo, Norway

来源：

2010 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE 2010) | 2010年

关键词：

NEURAL-NETWORKS; FAULT-DIAGNOSIS; BOXES;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页数：6

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