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In Hoffman F, Hand DJ, Adams N, Fisher D & Guimaraes G (Eds.) Lecture Notes in Computer Science 2189: Advances in Intelligent Data Analysis, Fourth International
 

Summary: In Hoffman F, Hand DJ, Adams N, Fisher D & Guimaraes G (Eds.) Lecture Notes in
Computer Science 2189: Advances in Intelligent Data Analysis, Fourth International
Conference (IDA-01),2001, Cascais Portugal. Springer Verlag: Berlin.
Analyzing Data Clusters: A Rough Set Approach to
Extract Cluster-Defining Symbolic Rules
Syed Sibte Raza Abidi, Kok Meng Hoe, Alwyn Goh
School of Computer Science, Universiti Sains Malaysia, 11800 Penang, Malaysia
{sraza, kmhoe, alwyn}@cs.usm.my
Abstract. In this paper we present a strategy together with its computational
implementation to intelligently analyze data clusters in terms of symbolic
cluster-defining rules. We present a symbolic rule extraction workbench that
leverages rough set theory to inductively extract CNF form symbolic rules from
un-annotated 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 set approximation. The featured symbolic rule extraction workbench will
be tested and analyzed using several well-known biomedical datasets.
1. Introduction
The on-going information revolution is generating volumes of data, from sources
as diverse as banking transactions, scientific explorations, telecommunication

  

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

 

Collections: Computer Technologies and Information Sciences