Characterization and detection of noise in clustering
Pattern Recognition Letters
Spatial models for fuzzy clustering
Computer Vision and Image Understanding
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
A Comparative Study of RNN for Outlier Detection in Data Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
A Novel Approach to Noise Clustering for Outlier Detection
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special issue on soft computing for information mining
A new segmentation system for brain MR images based on fuzzy techniques
Applied Soft Computing
A kernelized fuzzy c-means algorithm for automatic magnetic resonance image segmentation
Journal of Computational Methods in Sciences and Engineering
A fast and robust image segmentation using FCM with spatial information
Digital Signal Processing
Image Segmentation Based on Adaptive Fuzzy-C-Means Clustering
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Identifying single good clusters in data sets
IWICPAS'06 Proceedings of the 2006 Advances in Machine Vision, Image Processing, and Pattern Analysis international conference on Intelligent Computing in Pattern Analysis/Synthesis
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Generalized fuzzy c-means clustering strategies using Lp norm distances
IEEE Transactions on Fuzzy Systems
Expert Systems with Applications: An International Journal
Hi-index | 0.00 |
The main motivation of this paper is to introduce a class of robust non-Euclidean distance measures for the original data space to derive new objective function and thus clustering the non-Euclidean structures in data to enhance the robustness of the original clustering algorithms to reduce noise and outliers. The new objective functions of proposed algorithms are realized by incorporating the noise clustering concept into the entropy based fuzzy C-means algorithm with suitable noise distance which is employed to take the information about noisy data in the clustering process. This paper presents initial cluster prototypes using prototype initialization method, so that this work tries to obtain the final result with less number of iterations. To evaluate the performance of the proposed methods in reducing the noise level, experimental work has been carried out with a synthetic image which is corrupted by Gaussian noise. The superiority of the proposed methods has been examined through the experimental study on medical images. The experimental results show that the proposed algorithms perform significantly better than the standard existing algorithms. The accurate classification percentage of the proposed fuzzy C-means segmentation method is obtained using silhouette validity index.