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A Comparative Fuzzy Cluster Analysis of the Binder’s Performance Grades Using Fuzzy Equivalence Relation via Different Distance Measures

  • Rajesh Kumar Chandrawat
  • Rakesh KumarEmail author
  • Varinda Makkar
  • Manisha Yadav
  • Pratibha Kumari
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 955)

Abstract

The aim of this paper is to classify the performance grades of binders for NCHRP 90-07 using fuzzy equivalence clustering via Minkowski, Mahalanobis, Cosine, Chebychev and Correlation distance function. The performances of binders were graded in terms of high specific and equal stiffness temperature at three different parameters. The five distance functions namely Minskowski \( \left( {w = 2} \right) \), Mahalonobis, Cosine, Chebychev and Correlation are successfully applied in the clustering methodology to achieve a better separation analysis. The clusters are discovered by all five distances and distinguished for suitable value of membership grade. We also include a theoretical comparison between the clustering performances by these distances. The Mahalonobis distance function trialed first time in the equivalence fuzzy clustering methodology and accomplished the desirable objectives. The core effectuations of Mahalonobis and Chebychev distance over other four distances on the clustering performance of binders are investigated.

Keywords

Fuzzy equivalence class clustering Binders high specific temperature Distance function 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Rajesh Kumar Chandrawat
    • 1
  • Rakesh Kumar
    • 1
    Email author
  • Varinda Makkar
    • 1
  • Manisha Yadav
    • 1
  • Pratibha Kumari
    • 1
  1. 1.Lovely Professional UniversityPunjabIndia

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