Advanced search

Grey cluster evaluation models based on mixed triangular whitenization weight functions

Si-feng Liu (Institute for Grey Systems Studies, Nanjing University of Aeronautics and Astronautics, Nanjing, China AND Centre for Computational Intelligence, De Montfort University, Leicester, UK)
Yingjie Yang (De Montfort University)
Zhi-geng Fang (Institute for Grey Systems Studies, Nanjing University of Aeronautics and Astronautics, Nanjing, China)
Naiming Xie (Institute for Grey Systems Studies, Nanjing University of Aeronautics and Astronautics, Nanjing, China)

Grey Systems: Theory and Application

ISSN: 2043-9377

Publication date: 2 November 2015

Abstract

Purpose

The purpose of this paper is to present two novel grey cluster evaluation models to solve the difficulty in extending the bounds of each clustering index of grey cluster evaluation models.

Design/methodology/approach

In this paper, the triangular whitenization weight function corresponding to class 1 is changed to a whitenization weight function of its lower measures, and the triangular whitenization weight function corresponding to class s is changed to a whitenization weight function of its upper measures. The difficulty in extending the bound of each clustering indicator is solved with this improvement.

Findings

The findings of this paper are the novel grey cluster evaluation models based on mixed centre-point triangular whitenization weight functions and the novel grey cluster evaluation models based on mixed end-point triangular whitenization weight functions.

Practical implications

A practical evaluation and decision problem for some projects in a university has been studied using the new triangular whitenization weight function.

Originality/value

Particularly, compared with grey variable weight clustering model and grey fixed weight clustering model, the grey cluster evaluation model using whitenization weight function is more suitable to be used to solve the problem of poor information clustering evaluation. The grey cluster evaluation model using endpoint triangular whitenization weight functions is suitable for the situation that all grey boundary is clear, but the most likely points belonging to each grey class are unknown; the grey cluster evaluation model using centre-point triangular whitenization weight functions is suitable for those problems where it is easier to judge the most likely points belonging to each grey class, but the grey boundary is not clear.

Keywords

Acknowledgements

This research is supported by a Marie Curie International Incoming Fellowship within the 7th European Community Framework Programme (Grant No. FP7-PIIF-GA-2013-629051), the National Natural Science Foundation of China (91324003), the joint research project of both the NSFC (71111130211) and the RS of UK, the Leverhulme Trust international network research project on Grey systems. At the same time, the authors would like to acknowledge the partial support of the Fundamental Research Funds for the Central Universities (NP2015208), and the Foundation for national outstanding teaching group of China (No. 10td128).

Citation

Liu, S.-f., Yang, Y., Fang, Z.-g. and Xie, N. (2015), "Grey cluster evaluation models based on mixed triangular whitenization weight functions", Grey Systems: Theory and Application, Vol. 5 No. 3, pp. 410-418. https://doi.org/10.1108/GS-11-2014-0050

Download as .RIS

Publisher

:

Emerald Group Publishing Limited

Copyright © 2015, Emerald Group Publishing Limited

To read the full version of this content please select one of the options below

You may be able to access this content by logging in via Shibboleth, Open Athens or with your Emerald account.
To rent this content from Deepdyve, please click the button.
Rent from Deepdyve
If you think you should have access to this content, click the button to contact our support team.
Contact us
Manage cookies

We’re listening — tell us what you think

Join us on our journey