TY - JOUR
T1 - Data Description Through Information Granules
T2 - A Multiview Perspective
AU - Balamash, Abdullah
AU - Pedrycz, Witold
AU - Al-Hmouz, Rami
AU - Morfeq, Ali
N1 - Funding Information:
This project was funded by the Deanship of Scientific Research (DSR), King Abdulaziz University, Jeddah, Saudi Arabia, under Grant No. (KEP-5-135-39). The authors, therefore, acknowledge with thanks DSR technical and financial support.
Publisher Copyright:
© 2020, Taiwan Fuzzy Systems Association.
PY - 2020/9/1
Y1 - 2020/9/1
N2 - 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.
AB - 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.
KW - Classification
KW - Clustering
KW - Granular signature of data
KW - Information granules
KW - Multiview perspective
KW - Prediction
KW - Reconstruction
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U2 - 10.1007/s40815-020-00903-z
DO - 10.1007/s40815-020-00903-z
M3 - Article
AN - SCOPUS:85088665484
SN - 1562-2479
VL - 22
SP - 1731
EP - 1747
JO - International Journal of Fuzzy Systems
JF - International Journal of Fuzzy Systems
IS - 6
ER -