Data Description Through Information Granules: A Multiview Perspective

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

In light of the remarkable diversity of data, arises an interesting and challenging problem of their description and concise interpretation. In a nutshell, in the proposed description pursued in this study, we consider a framework of information granules. The study develops a general scheme composed of two functional phases: (i) clustering data and features forming segments of original data and delivering a meaningful partition of data, and (ii) development of information granules. In both phases, we discuss a suite of performance indexes quantifying the quality of segments of data and the resulting information granules. Along this line, discussed are collections of information granules and their mutual relationships. A series of publicly available data sets is used in the experiments—their granular signature is quantified, and the quality of these findings is analyzed.

Original languageEnglish
Pages (from-to)1731-1747
Number of pages17
JournalInternational Journal of Fuzzy Systems
Volume22
Issue number6
DOIs
Publication statusPublished - Sep 1 2020
Externally publishedYes

Keywords

  • Classification
  • Clustering
  • Granular signature of data
  • Information granules
  • Multiview perspective
  • Prediction
  • Reconstruction

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Computational Theory and Mathematics
  • Artificial Intelligence

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