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Application of Rough Sets in Pattern Recognition

  • Sushmita Mitra
  • Haider Banka
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4400)

Abstract

This article provides an overview of recent literature on some tasks of pattern recognition using rough sets and its hybridization with other soft computing paradigms. Rough set theory is an established tool for dealing with imprecision, noise, and uncertainty in data. In this article we will focus on two recent applications using rough sets; viz., feature selection in high dimensional gene expression data, and collaborative clustering. The experimental results demonstrate that the incorporation of rough set improves the performance of the system.

Keywords

Feature Selection Discernibility Matrix Cluster Prototype Indiscernibility Relation Redundancy Reduction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Sushmita Mitra
    • 1
  • Haider Banka
    • 1
  1. 1.Center for Soft Computing Research: A National Facility, Indian Statistical Institute, Kolkata 700 108India

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