Comparative Study of Euclidean and City Block Distances in Fuzzy C-Means Clustering Algorithm

Saratha Sathasivam and Abdu Masanawa Sagir
Volume 1: Issue 1, Revised on – 30 March 2020, pp 11-15

Author's Information
Saratha Sathasivam1 
Corresponding Author
1University Sains Malaysia, School of Mathematical Sciences, Pinang, Malaysia.
saratha@usm.my

 Abdu Masanawa Sagir2
2University Sains Malaysia, School of Mathematical Sciences, Pinang, Malaysia.

Research Article -- Peer Reviewed
First online on – 30 Dec 2014,      Revised on – 30 March 2020

Open Access article under Creative Commons License

Cite this article –Saratha Sathasivam and Abdu Masanawa Sagir, “Comparative Study of Euclidean and City Block Distances in Fuzzy C-Means Clustering Algorithm”, International Journal of Computational and Electronics Aspects in Engineering, RAME Publishers, vol. 1, issue 1, pp. 11-15, 2014, Revised in 2020.
https://doi.org/10.26706/ijceae.1.1.20141203

Abstract:-
Fuzzy c-means algorithm is one of the most important partitioning techniques and widely used for data clustering and image segmentation. The choice of distance metrics has played key role in data clustering problems since distance metric is used to determine the similarities between data points. In this paper Fuzzy c-means algorithms uses Euclidean and City block distances for comparative analysis to measure the similarities between objects. The results for data clustering problems using Euclidean distance has shown good performance than City block distance in terms of computational time values and the quality of clusters obtained. Similarities, differences and applications of the two proposed distance metrics have been described.
Index Terms:-
City block distance, Clustering, Euclidean distance, Fuzzy c-Means
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