Advances in Fuzzy Clustering and its Applications
By José Valente de Oliveira, Witold Pedrycz. 2007
493 citations recorded by the source corpus.
DESCRIPTION A comprehensive, coherent, and in depth presentation of the state of the art in fuzzy clustering. Fuzzy clustering is now a mature and vibrant area of research with highly innovative advanced applications. Encapsulating this through presenting a careful selection of research contributions, this book addresses timely and relevant concepts and methods, whilst identifying major challenges and recent developments in the area. Split into five clear sections, Fundamentals, Visualization, Algorithms and Computational Aspects, Real-Time and Dynamic Clustering, and Applications and Case Studies, the book covers a wealth of novel, original and fully updated material, and in particular offers:
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/1b8bc0bfd8051f63cba76c997b368f5b15b2ac53
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In Artificial Intelligence
In Computer Vision and Pattern Recognition
Citations in the corpus
- Analysis and Forecasting of the Energy Consumption in Wastewater Treatment Plant (2019, methodology): “As wastewater treatment system contains many statistics about energy consumption, and FCM is an efficient tool to divide a given set of data into categories so that elements drawn from the same type are as similar to each other as possible, while those assigned to different groups are dissimilar [28”
- Fuzzy clustering algorithms for unsupervised change detection in remote sensing images (2011, background): “Singularity occurs in FCM when one or more of the distancesk(xk vi)k = 0 at any iteration.”
- A Clustering-Based Graph Laplacian Framework for Value Function Approximation in Reinforcement Learning (2014, methodology): “The FCM algorithm [23], [25], [27] partitions the given dataset X into c fuzzy sets by minimizing the within-cluster variance”
- Segmentation of X‐ray computed tomography images of porous materials: A crucial step for characterization and quantitative analysis of pore structures (2009, methodology): “This problem can be alleviated with more robust possibilistic fuzzy clustering techniques [De Oliveira and Pedrycz, 2007].”