C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Data-Driven Discovery of Quantitative Rules in Relational Databases
IEEE Transactions on Knowledge and Data Engineering
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
An Integration of Cloud Transform and Rough Set Theory to Induction of Decision Trees
Fundamenta Informaticae - Fundamentals of Knowledge Technology
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Data classification is an important research topic in the field of data mining and knowledge discovery. There have been many data classification methods studied, including decision-tree method, statistical methods, neural networks, rough sets, etc. In this paper, we present a new mathematical representation of qualitative concepts-Cloud Models. With the new models, mapping between quantities and qualities becomes much easier and interchangeable. Based on the cloud models, a novel qualitative strategy for data classification in large relational databases is proposed. Then, the algorithms for classification are developed, such as cloud generation, complexity reduction, identifying interacting attributes, etc. Finally, we perform experiments on a challenging medical diagnosis domain, acute abdominal pain. The results show the advantages of the model in the process of knowledge discovery.