Data Description Through Information Granules: A Multiview Perspective
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Data Description Through Information Granules: A Multiview Perspective Abdullah Balamash3,4 • Witold Pedrycz1,2,3 • Rami Al-Hmouz3 • Ali Morfeq3
Received: 17 October 2019 / Revised: 25 May 2020 / Accepted: 9 June 2020 Taiwan Fuzzy Systems Association 2020
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. Keywords Information granules Multiview perspective Clustering Reconstruction Classification Prediction Granular signature of data
& Abdullah Balamash [email protected] 1
Department of Electrical & Computer Engineering, University of Alberta, Edmonton, AB T6R 2V4, Canada
2
Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland
3
Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia
4
Center of Excellence in Intelligent Engineering Systems (CEIES), King Abdulaziz University, Jeddah 21589, Saudi Arabia
1 Introduction Data analysis and data analytics, in general, are inherently aimed at revealing and description of interpretable and stable relationships among variables as well as quantifying their changes over time and space. Along with large volumes of data and their diversity, comes a genuine need to develop a flexible, user-centric and computationally efficient environment producing meaningful results. The key research hypothesis is that in the realization of the above stated agenda of data analytics, the concepts of a multiview perspective [1–4] of data with the use of information granules play a pivotal role both at the methodological as well as algorithmic level of ensuing constructs. The formation and engagement of the multiview organization of processing of data contributes in a tangible way to the efficient way of solving of a spectrum of tasks of data analysis, especially facilitating a thorough user-centric interpretation of results and producing readable yet fully legitimate outcomes supported by the existing experimental evidence. The varying (adjustable) perspective delivered by information granules helps establish a sound tradeoff between the representation capabilities of various views at the data and the efficiency of fundamental categories of tasks of data
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