Structure identification of fuzzy model
Fuzzy Sets and Systems
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mining fuzzy association rules
CIKM '97 Proceedings of the sixth international conference on Information and knowledge management
NEFCLASSmdash;a neuro-fuzzy approach for the classification of data
SAC '95 Proceedings of the 1995 ACM symposium on Applied computing
Principles of data mining
Binary Rule Generation via Hamming Clustering
IEEE Transactions on Knowledge and Data Engineering
Mining fuzzy association rules for classification problems
Computers and Industrial Engineering
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Influential Rule Search Scheme (IRSS)-A New Fuzzy Pattern Classifier
IEEE Transactions on Knowledge and Data Engineering
Fuzzy decision trees: issues and methods
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A comparative study on heuristic algorithms for generating fuzzydecision trees
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Comment on “Combinatorial rule explosion eliminated by a fuzzy rule configuration” [and reply]
IEEE Transactions on Fuzzy Systems
Fuzzy association rules: general model and applications
IEEE Transactions on Fuzzy Systems
Mining fuzzy association rules in a bank-account database
IEEE Transactions on Fuzzy Systems
A new methodology of extraction, optimization and application of crisp and fuzzy logical rules
IEEE Transactions on Neural Networks
Extracting compact and information lossless sets of fuzzy association rules
Fuzzy Sets and Systems
Towards healthy association rule mining (HARM): a fuzzy quantitative approach
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Fuzzy C-means method with empirical mode decomposition for clustering microarray data
International Journal of Data Mining and Bioinformatics
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Due to complexity of biomedical classification problems, it is impossible to build a perfect classifier with 100% prediction accuracy. Hence a more realistic target is to build an effective Decision Support System (DSS). Here 'effective' means that a DSS should not only predict unseen samples accurately, but also work in a human-understandable way. In this paper, we propose a novel adaptive Fuzzy Association Rules (FARs) mining algorithm, named FARM-DS, to build such a DSS for binary classification problems in the biomedical domain. In the training phase, four steps are executed to mine FARs, which are thereafter used to predict unseen samples in the testing phase. The new FARM-DS algorithm is evaluated on two publicly available medical datasets. The experimental results show that FARM-DS is competitive in terms of prediction accuracy. More importantly, the mined FARs provide strong decision support on disease diagnoses due to their easy interpretability.