Robust local feature weighting hard c-means clustering algorithm

Authors:
Xiaobin Zhi;Jiulun Fan;Feng Zhao
Affiliations:
School of Science, Xi' an University of Post and Telecommunications, Xi' an, China,School of Electronic Engineering, Xidian University, Xi' an, China;School of Communication and Information Engineering, Xi' an University of Post and Telecommunications, Xi' an, China;School of Communication and Information Engineering, Xi' an University of Post and Telecommunications, Xi' an, China
Venue:
IScIDE'11 Proceedings of the Second Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Year:
2011

Citing 5
Cited 0

Cluster detection in background noise

Pattern Recognition
Mean Shift: A Robust Approach Toward Feature Space Analysis

IEEE Transactions on Pattern Analysis and Machine Intelligence
Automated Variable Weighting in k-Means Type Clustering

IEEE Transactions on Pattern Analysis and Machine Intelligence
Localized feature selection for clustering

Pattern Recognition Letters
Enhanced soft subspace clustering integrating within-cluster and between-cluster information

Pattern Recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

In view of local feature weighting hard C-means (LWHCM) clustering algorithm sensitive to noise, based on a non-Euclidean metric, a robust local feature weighting hard C-means (RLWHCM) clustering algorithm is presented. The robustness of RLWHCM is analyzed by using the location M-estimate in robust statistical theory. By endowing each data point with a dynamic weighting function on each feature of data point, RLWHCM can estimate the clustering center more accurately in noisy environment. Experimental results on synthetic and real world data sets demonstrate the advantages of RLWHCM over LWHCM.