C4.5: programs for machine learning
C4.5: programs for machine learning
Data mining: building competitive advantage
Data mining: building competitive advantage
Data mining: concepts and techniques
Data mining: concepts and techniques
Principles of data mining
Data Mining: Introductory and Advanced Topics
Data Mining: Introductory and Advanced Topics
SLIQ: A Fast Scalable Classifier for Data Mining
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
An Interval Classifier for Database Mining Applications
VLDB '92 Proceedings of the 18th International Conference on Very Large Data Bases
Theoretical Comparison between the Gini Index and Information Gain Criteria
Annals of Mathematics and Artificial Intelligence
Data Mining
A new binary classifier: clustering-launched classification
ICIC'06 Proceedings of the 2006 international conference on Intelligent computing: Part II
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Classification is one of the core topics in data mining technologies. This paper studies the geometry of classifying convex clusters based on support functionals in the dual spaces. For the convex clusters that are to be classified, a combination of linear discriminant functions could solve the problem. The geometrical depiction of linear discriminant functions and supporting hyperplanes for the convex clusters help to characterize the relations of the convex clusters, and the distances to the convex clusters and complement of convex clusters calibrate the measures between the support functionals and convex clusters. Examples are given.