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

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 have 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.