Histogram ranking with generalised similarity-based TOPSIS applied to patent ranking Online publication date: Thu, 10-Mar-2016
by Pasi Luukka; Mikael Collan
International Journal of Operational Research (IJOR), Vol. 25, No. 4, 2016
Abstract: In this paper, we introduce a new version of the well-known TOPSIS method: similarity-based TOPSIS. The new method uses a similarity measure to replace the commonly used distance measure. Similarity measure is formed under generalised Łukasiewicz structure that allows us to form the measure in a more general structure and this way enhances (patent) ranking results. At the same time, however, the selection of a similarity parameter becomes a problem. To fix this new problem, a new method that we call histogram ranking is introduced. Histogram ranking is usable for relaxing the dependence of ranking on parameter value; it is designed to be a complement to parameter dependent ranking methods and is usable, when it is difficult to select precise parameter values. Histogram ranking is based on calculating the centre of gravity points from the histograms and this information is then used to form parameter value independent ranking of the object.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Operational Research (IJOR):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com