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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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Speedup of Interval Type 2 Fuzzy Logic Systems Based on GPU for Robot Navigation., , , and . Adv. Fuzzy Syst., (2012)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)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)Advanced Semi-Supervised Possibilistic Fuzzy C-means Clustering Using Spatial-Spectral Distance for Land-Cover Classification., , and . SMC, page 4375-4380. IEEE, (2018)A New Assessment of Cluster Tendency Ensemble approach for Data Clustering., , , and . SoICT, page 216-221. ACM, (2018)Approaching semi-supervised collaborative learning model for remote sensing image analysis., , and . RIVF, page 548-553. IEEE, (2022)Feature-reduction fuzzy co-clustering algorithm for hyperspectral image segmentation., , and . FUZZ-IEEE, page 1-6. IEEE, (2017)Land cover classification using interval type-2 fuzzy clustering for multi-spectral satellite imagery., and . SMC, page 2371-2376. IEEE, (2012)Refinement CTIN for general type-2 fuzzy logic systems.. FUZZ-IEEE, page 1225-1232. IEEE, (2011)