C4.5: programs for machine learning
C4.5: programs for machine learning
The KDD process for extracting useful knowledge from volumes of data
Communications of the ACM
Software metrics (2nd ed.): a rigorous and practical approach
Software metrics (2nd ed.): a rigorous and practical approach
Data mining: concepts and techniques
Data mining: concepts and techniques
Data Mining: Introductory and Advanced Topics
Data Mining: Introductory and Advanced Topics
Discretization: An Enabling Technique
Data Mining and Knowledge Discovery
Machine Learning
Potter's Wheel: An Interactive Data Cleaning System
Proceedings of the 27th International Conference on Very Large Data Bases
Knowledge Discovery in Databases: An Attribute-Oriented Approach
VLDB '92 Proceedings of the 18th International Conference on Very Large Data Bases
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Detecting noisy instances with the rule-based classification model
Intelligent Data Analysis
Data mining in soft computing framework: a survey
IEEE Transactions on Neural Networks
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Currently, there are many data preprocess methods, such as data discretization, data cleaning, data integration and transformation, data reduction ... etc. Concept hierarchies are a form of data discretization that can use for data preprocessing. Using discrete data are usually more compact, shorter and more quickly than using continuous ones. So that we proposed a data discretization method, which is the modified minimize entropy principle approach to fuzzify attribute and then build the classification tree. For verification, two NASA software projects KC2 and JM1 are applied to illustrate our proposed method. We establish a prototype system to discrete data from these projects. The error rate and number of rules show that the proposed approaches are both better than other methods.