A novel framework of the fuzzy c-means distances problem based weighted distance

A novel framework of the fuzzy c-means distances problem based weighted distance Andy Arief Setyawan a,1,, Ahmad Ilham b,1 a Department of Information and Communication, Pemalang District Government, Pemalang, Indonesia b Department of Informatics, Universitas Muhammadiyah Semarang, Semarang 50354, Indonesia Abstract 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 propose 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. Experimental result using the UCI data set show the proposed method is superior to the original method and other clustering methods. Keywords: clustering, fuzzy c-means, euclidean distance, weighted distance, canberra distance 1. Introduction Cluster analysis or clustering is the process of partitioning a set of data objects into subset or clusters, where the objects in a cluster is similar to onenull This document is a collaborative effort by Intelligent Systems Research Group Indonesia and Informatics Department Universitas Muhammadiyah Semarang.