Fuzzy C-means based clustering for linearly and nonlinearly separable data

Authors:
Du-Ming Tsai;Chung-Chan Lin
Affiliations:
Department of Industrial Engineering & Management, Yuan-Ze University, 135 Yuan-Tung Road, Nei-Li, Tao-Yuan, Taiwan, ROC;Department of Industrial Engineering & Management, Yuan-Ze University, 135 Yuan-Tung Road, Nei-Li, Tao-Yuan, Taiwan, ROC
Venue:
Pattern Recognition
Year:
2011

Citing 10
Cited 6

Nonlinear component analysis as a kernel eigenvalue problem

Neural Computation
Pattern Recognition with Fuzzy Objective Function Algorithms

Pattern Recognition with Fuzzy Objective Function Algorithms
Clustering Incomplete Data Using Kernel-Based Fuzzy C-means Algorithm

Neural Processing Letters
Improving fuzzy c-means clustering based on feature-weight learning

Pattern Recognition Letters
Combining Multiple Clusterings Using Evidence Accumulation

IEEE Transactions on Pattern Analysis and Machine Intelligence
A tutorial on spectral clustering

Statistics and Computing
Bootstrapping approach to feature-weight selection in fuzzy c-means algorithms with an application in color image segmentation

Pattern Recognition Letters
Rapid and brief communication: Evaluation of the performance of clustering algorithms in kernel-induced feature space

Pattern Recognition
A novel kernelized fuzzy C-means algorithm with application in medical image segmentation

Artificial Intelligence in Medicine
Mercer kernel-based clustering in feature space

IEEE Transactions on Neural Networks

Evaluating nonlinear variability of mental fatigue behavioral indices during long-term attentive task

Complexity
LinkFCM: Relation integrated fuzzy c-means

Pattern Recognition
FUAT - A fuzzy clustering analysis tool

Expert Systems with Applications: An International Journal
Robust kernelized approach to clustering by incorporating new distance measure

Engineering Applications of Artificial Intelligence
Software effort prediction using fuzzy clustering and functional link artificial neural networks

SEMCCO'12 Proceedings of the Third international conference on Swarm, Evolutionary, and Memetic Computing
A size-insensitive integrity-based fuzzy c-means method for data clustering

Pattern Recognition

Quantified Score

Hi-index	0.01

Visualization

Abstract

In this paper we present a new distance metric that incorporates the distance variation in a cluster to regularize the distance between a data point and the cluster centroid. It is then applied to the conventional fuzzy C-means (FCM) clustering in data space and the kernel fuzzy C-means (KFCM) clustering in a high-dimensional feature space. Experiments on two-dimensional artificial data sets, real data sets from public data libraries and color image segmentation have shown that the proposed FCM and KFCM with the new distance metric generally have better performance on non-spherically distributed data with uneven density for linear and nonlinear separation.