PaintingClass: interactive construction, visualization and exploration of decision trees
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Attribute Clustering for Grouping, Selection, and Classification of Gene Expression Data
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
An Experiment with Fuzzy Sets in Data Mining
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part II
Integration of Fuzzy Logic in Data Mining to Handle Vagueness and Uncertainty
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
Expert Systems with Applications: An International Journal
Mining changes in association rules: a fuzzy approach
Fuzzy Sets and Systems
A meta-heuristic approach for improving the accuracy in some classification algorithms
Computers and Operations Research
A fuzzy threshold based modified clustering algorithm for natural data exploration
PAISI'10 Proceedings of the 2010 Pacific Asia conference on Intelligence and Security Informatics
A fuzzy classification system based on Ant Colony Optimization for diabetes disease diagnosis
Expert Systems with Applications: An International Journal
Information Technology and Management
| Hi-index | 0.01 |
algorithms adopt either a decision tree based approach or an approach that requires users to provide some user-specifiedthresholds to guide the search for interesting rules. In this paper, we propose a new approach based on the use of an objective interestingness measure todistinguish interesting rules from uninteresting ones. Using linguistic terms to represent the revealed regularities and exceptions, this approach s especially useful when the discovered rules are presented to human experts for examination because of the affinity with thehuman knowledge representation. The use of fuzzy technique allows the predict on of attribute values to be associated with degree of membership. Our approach s, therefore, able to deal with the cases that an object can belong to more than one class. For example, a person can suffer from cold and fever to certain extent at the same time. Furthermore, our approach is more resilient to noise and missing data values because of the use of fuzzy technique. To evaluate the performance of our approach, we tested it using several real-life databases. The experimental results show that it can be very effective at data mining tasks. In fact, when compared to popular data mining algorithms, our approach can be better ableto uncover useful rules hidden in databases.