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GPU-based acceleration of interval type-2 fuzzy c-means clustering for satellite imagery land-cover classification.

, , and . ISDA, page 992-997. IEEE, (2012)

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Interpolative reasoning approach to sparse general type-2 fuzzy rules based on the reduced grid representation., , and . SoCPaR, page 72-78. IEEE, (2013)General Semi-supervised Possibilistic Fuzzy c-Means clustering for Land-cover Classification., and . KSE, page 1-6. IEEE, (2019)Semi-supervising Interval Type-2 Fuzzy C-Means clustering with spatial information for multi-spectral satellite image classification and change detection., , and . Comput. Geosci., (2015)Speedup of Interval Type 2 Fuzzy Logic Systems Based on GPU for Robot Navigation., , , and . Adv. Fuzzy Syst., (2012)Features reduction collaborative fuzzy clustering for hyperspectral remote sensing images analysis., , , , and . J. Intell. Fuzzy Syst., 45 (5): 7739-7752 (November 2023)A genetic type-2 fuzzy C-means clustering approach to M-FISH segmentation., , and . J. Intell. Fuzzy Syst., 27 (6): 3111-3122 (2014)Learning Rule for TSK Fuzzy Logic Systems Using Interval Type-2 Fuzzy Subtractive Clustering., , and . SEAL, volume 7673 of Lecture Notes in Computer Science, page 430-439. Springer, (2012)Approaching semi-supervised collaborative learning model for remote sensing image analysis., , and . RIVF, page 548-553. IEEE, (2022)A New Assessment of Cluster Tendency Ensemble approach for Data Clustering., , , and . SoICT, page 216-221. ACM, (2018)Advanced Semi-Supervised Possibilistic Fuzzy C-means Clustering Using Spatial-Spectral Distance for Land-Cover Classification., , and . SMC, page 4375-4380. IEEE, (2018)