C4.5: programs for machine learning
C4.5: programs for machine learning
Readings in Machine Learning
Knowledge Discovery in Databases: An Attribute-Oriented Approach
VLDB '92 Proceedings of the 18th International Conference on Very Large Data Bases
Analyzing Data Clusters: A Rough Sets Approach to Extract Cluster-Defining Symbolic Rules
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
Diagnosing Acute Appendicitis with Very Simple Classification Rules
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
ICTAI '97 Proceedings of the 9th International Conference on Tools with Artificial Intelligence
Advances in Engineering Software
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Rule induction is a data mining process for acquiring knowledge in terms of symbolic decision rules that explain the data in terms of causal relationship between conditional factors and a given decision/outcome. We present a Decision Rule Acquisition Workbench (DRAW) that discovers symbolic decision rules, in CNF form, from un-annotated data-sets. Our rule-induction strategy involves three phases: (a) conceptual clustering to cluster and generate a conceptual hierarchy of the data-set; (b) rough sets based rule induction algorithm to generate decision rules from the emergent data clusters; and (c) attribute oriented induction to generalize the derived decision rules to yield high-level decision rules and a minimal rule-set size. We evaluate DRAW with five standard machine learning datasets and apply to derive decision rules to understand optic nerve images in the realm of glaucoma decision support.