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IEEE Symposium on Computer Based Medical Systems (CBMS'2002), Maribor (Slovenia). Symbolic Exposition of Medical Data-Sets: A Data Mining
 

Summary: In 15th
IEEE Symposium on Computer Based Medical Systems (CBMS'2002), Maribor (Slovenia).
Symbolic Exposition of Medical Data-Sets: A Data Mining
Workbench to Inductively Derive Data-Defining Symbolic Rules
Syed Sibte Raza Abidi Kok Meng Hoe
Faculty of Computer Science, Dalhousie
University, Halifax B3H 1W5, Canada
Email: sraza@cs.dal.ca
School of Computer Science,
Universiti Sains Malaysia,
11800 Penang, Malaysia
Abstract
The application of data mining techniques upon medical data is certainly beneficial for
researchers interested in discerning the complexity of healthcare processes in real-life
operational situations. In this paper we present a methodology, together with its
computational implementation, for the automated extraction of data-defining CNF symbolic
rules from medical data-sets comprising both annotated and un-annotated attributes. We
propose a hybrid approach for symbolic rule extraction which features a sequence of methods
including data clustering, data discretization and eventually symbolic rule discovery via rough
set approximation. We present a generic data mining workbench that can generate

  

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

 

Collections: Computer Technologies and Information Sciences