Unsupervised feature selection using a neuro-fuzzy approach
Pattern Recognition Letters
Multiresolution Color Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Weighting in k-Means Clustering
Machine Learning
A novel initialization scheme for the fuzzy c-means algorithm for color clustering
Pattern Recognition Letters
Improving fuzzy c-means clustering based on feature-weight learning
Pattern Recognition Letters
Analysis of the weighting exponent in the FCM
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Optimality test for generalized FCM and its application to parameter selection
IEEE Transactions on Fuzzy Systems
Mathematical and Computer Modelling: An International Journal
Unsupervised feature evaluation: a neuro-fuzzy approach
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
International Journal of Computational Intelligence in Bioinformatics and Systems Biology
Gaussian mixture learning via robust competitive agglomeration
Pattern Recognition Letters
A gravitational approach to edge detection based on triangular norms
Pattern Recognition
Fuzzy C-means based clustering for linearly and nonlinearly separable data
Pattern Recognition
Weight selection in W-K-means algorithm with an application in color image segmentation
Computers & Mathematics with Applications
Sample-weighted clustering methods
Computers & Mathematics with Applications
Spatial color image segmentation based on finite non-Gaussian mixture models
Expert Systems with Applications: An International Journal
Journal of Visual Communication and Image Representation
Hi-index | 0.10 |
The fuzzy c-means (FCM) algorithm is a popular fuzzy clustering method. It is known that an appropriate assignment to feature weights can improve the performance of FCM. In this paper, we use the bootstrap method proposed by Efron [Efron, B., 1979. Bootstrap methods: Another look at the jackknife. Ann. Statist. 7, 1-26] to select feature weights based on statistical variations in the data. It is simple to compute and interpret for feature-weights selection. Compared with the feature weights proposed by Wang et al. [Wang, X.Z., Wang, Y.D., Wang, L.J., 2004. Improving fuzzy c-means clustering based on feature-weight learning. Pattern Recognition Lett. 25, 1123-1132], Modha and Spangler [Modha, D.S., Spangler, W.S., 2003. Feature weighting in k-means clustering. Machine Learn. 52, 217-237], Pal et al. [Pal, S.K., De, R.K., Basak, J., 2000. Unsupervised feature evaluation: A neuro-fuzzy approach. IEEE Trans. Neural Networks 11, 366-376] and Basak et al. [Basak, J., De, R.K., Pal, S.K., 1998. Unsupervised feature selection using a neuro-fuzzy approach. Pattern Recognition Lett. 19, 997-1006] we find that the proposed method provides a better clustering performance for Iris data and several simulated datasets based on error rate criterion and also performs well in color image segmentation according to Liu and Yang's [Liu, J., Yang, Y.H., 1994. Multiresolution color image segmentation technique. IEEE Trans. Pattern Anal. Machine Intell. 16, 689-700] evaluation function.