A novel grey fixed weight cluster model based on interval grey numbers. Issue 2 (7th August 2017)

• Record Type: Journal Article
• Authors: Ye, Jing; Dang, Yaoguo
• Is Part Of: Grey systems. Volume 7:Issue 2(2017)
• Page Start: 156
• Page End: 167
• Publication Date: 2017-08-07
• Subjects: Grey clustering evaluation -- Interval grey number -- Integral mean value function -- Triangular whitenization weight function; Cybernetics -- Periodicals; Systems engineering -- Periodicals; 003.5
• DOI: 10.1108/GS-10-2016-0040
• Languages: English
• ISSNs: 2043-9377

Abstract

Abstract : Purpose: Nowadays, evaluation objects are becoming more and more complicated. The interval grey numbers can be used to more accurately express the evaluation objects. However, the information distribution of interval grey numbers is not balanced. The purpose of this paper is to introduce the central-point triangular whitenization weight function to solve the clustering process of this kind of numbers. Design/methodology/approach: A new expression of the central-point triangular whitenization weight function is presented in this paper, in terms of the grey cluster problem based on interval grey numbers. By establishing the integral mean value function on the set of interval grey numbers, the application range of grey clustering model is extended to the interval grey number category, and, in this way, the grey fixed weight cluster model based on interval grey numbers is obtained. Findings: The model is verified by a case which reveals a high distinguishability, validity and practicability. Practical implications: This model can be used in many fields, such as agriculture, economy, geology and medical science, and provides a feasible method for evaluation schemes in performance evaluation, scheme selection, risk evaluation and so on. Originality/value: The central-point triangular whitenization weight function is introduced. The method reflects the thought "make full use of the information" in grey system theory and further enriches the system of grey clustering theory as well as expands the application scope of the grey clustering method.