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

## TL;DR

This paper introduces a new Canberra weighted distance metric to enhance the fuzzy c-means clustering algorithm, addressing issues with Euclidean distance in multidimensional and noisy data, and demonstrates improved performance on UCI datasets.

## Contribution

The paper proposes a novel Canberra weighted distance metric to improve FCM clustering accuracy over traditional Euclidean distance.

## Key findings

- Proposed method outperforms original FCM on UCI datasets.
- Canberra weighted distance reduces clustering errors in noisy data.
- Enhanced clustering results demonstrate the effectiveness of the new distance metric.

## 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 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.