Unsupervised Optimal Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
On Clustering Validation Techniques
Journal of Intelligent Information Systems
Clustering For Data Mining: A Data Recovery Approach (Chapman & Hall/Crc Computer Science)
Clustering For Data Mining: A Data Recovery Approach (Chapman & Hall/Crc Computer Science)
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Remote Sensing, Third Edition: Models and Methods for Image Processing
Remote Sensing, Third Edition: Models and Methods for Image Processing
On fuzzy cluster validity indices
Fuzzy Sets and Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An adaptive neuro-fuzzy system for automatic image segmentation and edge detection
IEEE Transactions on Fuzzy Systems
Image segmentation by histogram thresholding using fuzzy sets
IEEE Transactions on Image Processing
Automated computational delimitation of SST upwelling areas using fuzzy clustering
Computers & Geosciences
Lessons to learn from a mistaken optimization
Pattern Recognition Letters
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The Anomalous Pattern algorithm is explored as an initialization strategy to the Fuzzy K -Means (FCM), with the sequential extraction of clusters, that simultaneously allows the determination of the number of clusters. The composed algorithm, Anomalous Pattern Fuzzy Clustering (AP-FCM), is applied in the segmentation of Sea Surface Temperature (SST) images for the identification of Coastal Upwelling. A set of features are constructed from the AP-FCM clustering segmentation taking into account domain knowledge and a threshold procedure is defined in order to identify the transition cluster whose frontline is automatically annotated on SST images to separate the upwelling regions from the background. Two independent data samples in a total of 61 SST images covering large diversity of upwelling situations are analysed. Results show that by tuning the AP-FCM stop conditions it fits a good number of clusters providing an effective segmentation of the SST images whose spatial visualization of fuzzy membership closely reproduces the original images. Comparing the AP-FCM with the FCM using several validation indices shows the advantage of the AP-FCM avoiding under or over-segmented images. Quantitative assessment of the segmentations is accomplished through ROC analysis. Compared to FCM, the number of iterations of the AP-FCM is significantly decreased. The automatic annotation of upwelling frontlines from the AP-FCM segmentation overcomes the subjective visual inspection made by the Oceanographers.