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

Autor:	Setyawan, Andy Arief, Ilham, Ahmad
Rok vydání:	2019
Předmět:	Computer Science - Machine Learning Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: 25 pages, 6 figure, was submitted online submission at the Applied Computing and Informatics, Elsevier, July 18, 2019. King Saud University, Riyadh, Saudi Arabia
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1907.13513 Zobrazit plný text záznamu View this record from Arxiv