A novel framework of the fuzzy c-means distances problem based weighted distance
This is an incremental improvement for data mining applications like pattern recognition and image segmentation.
The authors tackled the problem of clustering errors in Fuzzy C-Means (FCM) algorithms caused by Euclidean distance, especially with multidimensional and noisy data, by proposing a Canberra Weighted Distance framework. Experimental results on UCI datasets showed the method outperformed the original FCM and other clustering approaches.
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.