Int. J. Fuzzy Computation and Modelling, Vol. 2, No. 3, 2017 261 Copyright © 2017 Inderscience Enterprises Ltd. Generalised intuitionistic fuzzy entropy and weighted correlation with application in multi-attributes decision-making Pratiksha Tiwari Delhi Institute of Advanced Studies, Plot No. 6, Sector 25, Rohini, Delhi, India Email: parth12003@yahoo.co.in Abstract: The present paper introduces generalised entropy for intuitionistic fuzzy entropy of order α with the evidences of its validity along with some of its properties. The proposed measure is a generalisation of the entropy given by De Luca and Termini (1972). It has been used to determine weights of both experts and attributes in intuitionistic fuzzy environment using decision matrices in multi-attributes decision-making problem with unknown weights. Further, weighted correlation coefficient is defined using the proposed entropy measure and correlation coefficients between alternatives and ideal point are determined. The value of correlation coefficient is used to rank the alternative and the alternative with greatest weighted correlation coefficient is selected as an optimal solution. Finally, an illustrative example describes its application on multi-attributes decision-making problem with undefined weights. Keywords: intuitionistic fuzzy set; IFS; intuitionistic fuzzy entropy; weights; weighted correlation coefficient; multi-attributes decision-making problem. Reference to this paper should be made as follows: Tiwari, P. (2017) ‘Generalised intuitionistic fuzzy entropy and weighted correlation with application in multi-attributes decision-making’, Int. J. Fuzzy Computation and Modelling, Vol. 2, No. 3, pp.261–274. Biographical notes: Pratiksha Tiwari is currently working as an Assistant Professor in the Department of Computer Application at the Delhi Institute of Advanced Studies. She is NET qualified and holds a PhD in Statistics from the M.D. University, Rohtak. Her areas of specialisation in teaching and research are fuzzy information theory, quantitative methods and business research. She has also presented papers in national and international seminars/conferences and published various research papers in refereed journals. 1 Introduction In fuzzy set theory, non-membership value of an element is complement of its membership value from one, but practically it is not true, this is dealt by higher order fuzzy set proposed by Atanassov (1986) termed as intuitionistic fuzzy sets (IFSs). It is found to be highly useful in dealing vagueness and hesitancy originated from inadequate information. It characterises two characteristic functions for membership and non-membership µ A (x) and υ A (x) respectively for an element of the universe of discourse. An IFS is given as A = {〈x, µ A (x), υ A (x)〉, x ∈ Ω} where µ A : Ω → [0, 1] and υ A : Ω → [0, 1]