C4.5: programs for machine learning
C4.5: programs for machine learning
Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Chi2: Feature Selection and Discretization of Numeric Attributes
TAI '95 Proceedings of the Seventh International Conference on Tools with Artificial Intelligence
IEEE Transactions on Neural Networks
Experiences Using Clustering and Generalizations for Knowledge Discovery in Melanomas Domain
ICDM '08 Proceedings of the 8th industrial conference on Advances in Data Mining: Medical Applications, E-Commerce, Marketing, and Theoretical Aspects
Review: Dimensionality reduction based on rough set theory: A review
Applied Soft Computing
Introducing intelligence in electronic healthcare systems: state of the art and future trends
Artificial intelligence
From clusters to rules: a hybrid framework for generalized symbolic rule induction
ICMLC'05 Proceedings of the 4th international conference on Advances in Machine Learning and Cybernetics
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We present a strategy, together with its computational implementation, to intelligently analyze the internal structure of inductively-derived data clusters in terms of symbolic cluster-defining rules. We present a symbolic rule extraction workbench that leverages rough sets theory to inductively extract CNF form symbolic rules from unannotated continuous-valued data-vectors. Our workbench purports a hybrid rule extraction methodology, incorporating a sequence of methods to achieve data clustering, data discretization and eventually symbolic rule discovery via rough sets approximation. The featured symbolic rule extraction workbench will be tested and analyzed using biomedical datasets.