Informational Paradigm, management of uncertainty and theoretical formalisms in the clustering framework
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TLDR
It is shown how all these clustering approaches are able of managing in different ways the uncertainty associated with the two components of the Informational Paradigm, i.e. the Empirical and Theoretical Information.About:
This article is published in Information Sciences.The article was published on 2017-08-01 and is currently open access. It has received 28 citations till now. The article focuses on the topics: Fuzzy clustering & Cluster analysis.read more
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A Novel Image Segmentation Algorithm Based on Neutrosophicsimilarity Clustering
Yanhui Guo,Abdulkadir Sengür +1 more
TL;DR: A novel algorithm based on neutrosophic similarity clustering (NSC) to segment gray level images is proposed and can process both images without noise and noisy images having different levels of noises well.
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Automated Delineation of Thyroid Nodules in Ultrasound Images Usingspatial Neutrosophic Clustering and Level Set
TL;DR: In this article, the authors proposed an automated delineation method that integrates spatial information with neutrosophic clustering and level-sets for accurate and effective segmentation of thyroid nodules in ultrasound images.
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A novel method for classification of BCI multi-class motor imagery task based on Dempster–Shafer theory
TL;DR: A novel method based on Dempster–Shafer theory to fuse the results of constituent binary classifiers is proposed and applied to the benchmark BCI competition iv set 2a data set, showing a significant improvement and proving success of the method in modeling uncertainty.
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Fuzzy clustering of fuzzy data based on robust loss functions and ordered weighted averaging
Pierpaolo D'Urso,Jacek M. Leski +1 more
TL;DR: This paper has proposed a robust fuzzy c-medoids clustering method for fuzzy data based on the combination of Huber's M-estimators and Yager's OWA (Ordered Weighted Averaging) operators, able to smooth the influence of anomalous data by means of a suitable parameter, the so-called typicality parameter.
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Evolutionary optimized fuzzy reasoning with mined diagnostic patterns for classification of breast tumors in ultrasound
TL;DR: A novel CAD approach for BUS data with human-in-the-loop is proposed, which skips the image processing by utilizing the artificial scoring datasets, and the BUS images from different sources can be classified.
References
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