Affiliations: [a]
School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, China
| [b]
Department of Information Engineering, Chuzhou Polytechnic, Chuzhou, China
| [c]
Department of Electrical and Control Engineering, Research Institute of Zhejiang University-Taizhou, Taizhou, China
Correspondence:
[*]
Corresponding author. Haoxiang Zhou, Department of Electrical and Control Engineering, Research Institute of Zhejiang University-Taizhou, Taizhou 318000, China. E-mail: zhx05150515@163.com.
Abstract: The fuzzy c-mean (FCM) clustering algorithm is a typical algorithm using Euclidean distance for data clustering and it is also one of the most popular fuzzy clustering algorithms. However, FCM does not perform well in noisy environments due to its possible constraints. To improve the clustering accuracy of item varieties, an improved fuzzy c-mean (IFCM) clustering algorithm is proposed in this paper. IFCM uses the Euclidean distance function as a new distance measure which can give small weights to noisy data and large weights to compact data. FCM, possibilistic C-means (PCM) clustering, possibilistic fuzzy C-means (PFCM) clustering and IFCM are run to compare their clustering effects on several data samples. The clustering accuracies of IFCM in five datasets IRIS, IRIS3D, IRIS2D, Wine, Meat and Apple achieve 92.7%, 92.0%, 90.7%, 81.5%, 94.2% and 88.0% respectively, which are the highest among the four algorithms. The final simulation results show that IFCM has better robustness, higher clustering accuracy and better clustering centers, and it can successfully cluster item varieties.
Keywords: Fuzzy clustering, FCM, PCM, Euclidean distance, distance function
