Uncertainty and grey data analytics

Abstract

Purpose The purpose of this paper is to propose a framework for data analytics where everything is grey in nature and the associated uncertainty is considered as an essential part in data collection, profiling, imputation, analysis and decision making. Design/methodology/approach A comparative study is conducted between the available uncertainty models and the feasibility of grey systems is highlighted. Furthermore, a general framework for the integration of grey systems and grey sets into data analytics is proposed. Findings Grey systems and grey sets are useful not only for small data, but also big data as well. It is complementary to other models and can play a significant role in data analytics. Research limitations/implications The proposed framework brings a radical change in data analytics. It may bring a fundamental change in our way to deal with uncertainties. Practical implications The proposed model has the potential to avoid the mistake from a misleading data imputation. Social implications The proposed model takes the philosophy of grey systems in recognising the limitation of our knowledge which has significant implications in our way to deal with our social life and relations. Originality/value This is the first time that the whole data analytics is considered from the point of view of grey systems.

Application of grey clusters in the development of a Synthetic Model of the goals of Polish family enterprises' successors

“…Using this methodology, one may forecast future behaviour of the system, assess the co-dependence of observation vectors and assess the efficiency of response to potential situations and make optimum decisions in this respect, as well as group them and study concentration (Cempel, 2014;Dong et al, 2019). Nowadays, grey theory has been widely used to solve many practical problems of science, technology, economy and social interactions (education, health care) (Liu et al, 2011(Liu et al, , 2013(Liu et al, , 2016bYang et al, 2019). Grey correlation analysis is an important part of the grey system and constitutes the basis for grey modelling (Xie et al, 2016).…”

A Novel Multi-Criteria Sorting Model Based on AHP-Entropy Grey Clustering for Dealing with Uncertain Incoming Core Quality in Remanufacturing Systems

“…Different from fuzzy sets, a rough set approximates a set of numbers using two definable upper and lower sets. In terms of data granularity (referring to the quality of information), a rough set is described and a finer granularity provides a more accurate approximation [15]. Meanwhile, the grey system approach has many attractive features, notably, to support for any data distribution requirement with a small data size.…”

“…Step 2. Calculation of the entropy (E r ) and the degree of diversity (d r ) Afterwards, entropy within the data of the normalized decision matrix for the r th criterion can be obtained using Equation (15). Meanwhile, the results are given in Table 14.…”

“…The divergence (d r ) and entropy weights (w e ro ) of each criterion for each incoming core. Finally, according to the AHP global weights w h ro which are equal to η ro in Table 11 and the entropy weights (w e ro ) in Equation ( 18), the weight of each criterion can be acquired on the principle of combined weighting methods as established by Equation (15). The weights of each criterion were derived by the combined weighting methods (w c ro ) given in Table 16.…”

“…In 1982, grey system theory (GST) was first proposed by Professor Julong Deng from Huazhong University of Science and Technology as a quantitative model for limited and incomplete data [15]. Incompleteness and inadequacy of data is a basic feature of uncertain systems [16].…”