Nonlinear strict distance and similarity measures for intuitionistic fuzzy sets with applications to pattern classification and medical diagnosis.

In this paper, we propose a new type of nonlinear strict distance and similarity measures for intuitionistic fuzzy sets (IFSs). Our proposed methods not only have good properties, but also improve the drawbacks proposed by Mahanta and Panda (Int J Intell Syst 36(2):615-627, 2021) in which, for example, their distance value of [Formula: see text] is always equal to the maximum value 1 for any intuitionistic fuzzy number [Formula: see text]. To resolve these problems in Mahanta and Panda (Int J Intell Syst 36(2):615-627, 2021), we establish a nonlinear parametric distance measure for IFSs and prove that it satisfies the axiomatic definition of strict intuitionistic fuzzy distances and preserves all advantages of distance measures.

Fuzzy Gaussian Lasso clustering with application to cancer data.

Recently, Yang et al. (2019) proposed a fuzzy model-based Gaussian (F-MB-Gauss) clustering that combines a model-based Gaussian with fuzzy membership functions for clustering. In this paper, we further consider the F-MB-Gauss clustering with the least absolute shrinkage and selection operator (Lasso) for feature (variable) selection, termed a fuzzy Gaussian Lasso (FG-Lasso) clustering algorithm.

Suppressed fuzzy-soft learning vector quantization for MRI segmentation.

Objective: A self-organizing map (SOM) is a competitive artificial neural network with unsupervised learning. To increase the SOM learning effect, a fuzzy-soft learning vector quantization (FSLVQ) algorithm has been proposed in the literature, using fuzzy functions to approximate lateral neural interaction of the SOM. However, the computational performance of FSLVQ is still not good enough, especially for large data sets.

Alpha-cut implemented fuzzy clustering algorithms and switching regressions.

In the fuzzy c-means (FCM) clustering algorithm, almost none of the data points have a membership value of 1. Moreover, noise and outliers may cause difficulties in obtaining appropriate clustering results from the FCM algorithm. The embedding of FCM into switching regressions, called the fuzzy c-regressions (FCRs), still has the same drawbacks as FCM.

Magnetic resonance imaging segmentation techniques using batch-type learning vector quantization algorithms.

In this article, we propose batch-type learning vector quantization (LVQ) segmentation techniques for the magnetic resonance (MR) images. Magnetic resonance imaging (MRI) segmentation is an important technique to differentiate abnormal and normal tissues in MR image data. The proposed LVQ segmentation techniques are compared with the generalized Kohonen's competitive learning (GKCL) methods, which were proposed by Lin et al.

A note on the ICS algorithm with corrections and theoretical analysis.

In [1], Ozdemir and Akarun proposed an intercluster separation (ICS) fuzzy clustering algorithm. The ICS algorithm is useful in combined quantization and dithering. However, there are two errors in the update equations for the ICS algorithm.

A similarity-based robust clustering method.

This paper presents an alternating optimization clustering procedure called a similarity-based clustering method (SCM). It is an effective and robust approach to clustering on the basis of a total similarity objective function related to the approximate density shape estimation. We show that the data points in SCM can self-organize local optimal cluster number and volumes without using cluster validity functions or a variance-covariance matrix.

Generalized Kohonen's competitive learning algorithms for ophthalmological MR image segmentation.

Kohonen's self-organizing map is a two-layer feedforward competitive learning network. It has been used as a competitive learning clustering algorithm. In this paper, we generalize Kohonen's competitive learning (KCL) algorithm with fuzzy and fuzzy-soft types called fuzzy KCL (FKCL) and fuzzy-soft KCL (FSKCL).

Segmentation techniques for tissue differentiation in MRI of ophthalmology using fuzzy clustering algorithms.

This paper presents MRI segmentation techniques to differentiate abnormal and normal tissues in Ophthalmology using fuzzy clustering algorithms. Applying the best-known fuzzy c-means (FCM) clustering algorithm, a newly proposed algorithm, called an alternative fuzzy c-mean (AFCM), was used for MRI segmentation in Ophthalmology. These unsupervised segmentation algorithms can help Ophthalmologists to reduce the medical imaging noise effects originating from low resolution sensors and/or the structures that move during the data acquisition.