Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
Data mining: concepts and techniques
Data mining: concepts and techniques
A relational model of data for large shared data banks
Communications of the ACM
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Genetic Programming-Based Decision Trees for Software Quality Classification
ICTAI '03 Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence
A new hybrid ant colony optimization algorithm for feature selection
Expert Systems with Applications: An International Journal
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Decision Tree Induction (DTI), one of the Data Mining classification methods, is used in this research for predictive problem solving in analyzing patient medical track records. In this paper, we extend the concept of DTI dealing with meaningful fuzzy labels in order to express human knowledge for mining fuzzy association rules. Meaningful fuzzy labels (using fuzzy sets) can be defined for each domain data. For example, fuzzy labels poor disease, moderate disease, and severe disease are defined to describe a condition/type of disease. We extend and propose a concept of fuzzy information gain to employ the highest information gain for splitting a node. In the process of generating fuzzy association rules, we propose some fuzzy measures to calculate their support, confidence and correlation. The designed application gives a significant contribution to assist decision maker for analyzing and anticipating disease epidemic in a certain area.