  Clustering is one of the major roles in data mining that is widely
application in pattern recognition and image segmentation. Fuzzy C-means (FCM)
is the most used clustering algorithm that proven efficient, fast and easy to
implement, however, FCM uses the Euclidean distance that often leads to
clustering errors, especially when handling multidimensional and noisy data. In
the last few years, many distances metric have been proposed by researchers to
improve the performance of the FCM algorithms, and the majority of researchers
propose weighted distance. In this paper, we proposed Canberra Weighted
Distance to improved performance of the FCM algorithm. The experimental result
using the UCI data set show the proposed method is superior to the original
method and other clustering methods.
