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A HYBRID OF CONCEPTUAL CLUSTERS, ROUGH SETS AND ATTRIBUTE ORIENTED INDUCTION FOR INDUCING SYMBOLIC RULES
 

Summary: A HYBRID OF CONCEPTUAL CLUSTERS, ROUGH SETS AND ATTRIBUTE
ORIENTED INDUCTION FOR INDUCING SYMBOLIC RULES
QINGSHUANG JIANG, SYED SIBTE RAZA ABIDI
Faculty of Computer Science, Dalhousie University, Halifax B3H 1W5, Canada
E-MAIL: sraza@cs.dal.ca
Abstract:
Rule induction is a data mining process for
acquiring knowledge in terms of symbolic decision rules
from a number of specific 'examples' to explain the
inherent causal relationship between conditional factors
and a given decision/outcome. We present a Decision
Rule Acquisition Workbench (DRAW) that discovers
conjunctive normal form decision rules from
un-annotated data-sets. Our rule-induction strategy
uses (i) conceptual clustering to cluster and generate a
conceptual hierarchy of the data-set; (ii) rough sets
based rule induction algorithm to generate decision
rules from the emergent data clusters; and (iii) attribute
oriented induction to generalize the derived decision
rules to yield high-level decision rules and a minimal

  

Source: Abidi, Syed Sibte Raza - Faculty of Computer Science, Dalhousie University

 

Collections: Computer Technologies and Information Sciences