From data to granular data and granular classifiers

Rami Al-Hmouz, Witold Pedrycz, Abdullah Balamash, Ali Morfeq

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)


Information granules emerging as a result of an abstract and more condensed and global view at numeric data play an essential role in various pattern recognition pursuits. In this study, we investigate an idea of granular prototypes (representatives) and discuss their role in the realization of classification schemes. A two-stage procedure of a formation of information granules is discussed. We show how the commonly used clustering methods are viewed as a prerequisite for the construction of granular prototypes. In this regard, a certain version of the principle of justifiable granularity is investigated. In the sequel, a characterization of information granules expressed in terms of their information (classification) content is provided and its usage in the realization of a classifier is studied. Experimental studies involving both synthetic and publicly available data are reported.

Original languageEnglish
Title of host publicationProceedings of the 2014 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages7
ISBN (Electronic)9781479920723
Publication statusPublished - Sept 4 2014
Event2014 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2014 - Beijing, China
Duration: Jul 6 2014Jul 11 2014

Publication series

NameIEEE International Conference on Fuzzy Systems
ISSN (Print)1098-7584


Conference2014 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2014


  • clustering
  • Fuzzy C-Means
  • Granular Computing
  • information granules
  • pattern classification
  • principle of justifiable granularity

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Artificial Intelligence
  • Applied Mathematics


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