Intuitionistic Fuzzy c-Ordered Means Clustering Algorithm

Abstract

Atanassov intuitionistic fuzzy set (AIFS) has the capability to deal with various uncertain situations, so its popularity among researchers is quite high. It has been observed that Euclidean distance measure based AIFS clustering algorithms perform well on imprecise datasets. The performance of Euclidean measure based clustering algorithms deteriorates due to the presence of outliers/noise within a dataset. In the paper, an extension of the algorithm given by Leski, Jacek M. [Fuzzy Sets and Systems, 286 (2016): 114-133] is proposed as intuitionistic fuzzy c-ordered means clustering algorithm. This paper analyses the functionality of clustering algorithms over outliers/noises based datasets. In IFCOM, an alternate of Euclidean distance known as Loss function is used. Moreover, IFCOM uses intuitionistic fuzzy OWA to combat the ill effects of the noises and outliers. The proposed algorithm exploits a typicality function based weighing ordering approach. The approach assigins lower weights to the outliers. Hence, the catastrophic behavior is not observed in IFCOM while dealing outliers possessing synthetic and UCI machine learning datasets.

Distance Analysis and Dimensionality Reduction using PCA on Brain Tumour MRI Scans

“…Published in the Journal of King Saud University -Computer and Information Sciences, their work focuses on precise tumor delineation, demonstrating promising results in accurate segmentation and analysis. The technique employs the spatial Fuzzy C-Means algorithm, a semiautomated and interactive approach [4]. This algorithm successfully segments the tumor region in MRI brain images, providing valuable information for diagnosis and treatment planning.…”

Clustering and Markov Model of Plug-In Electric Vehicle Charging Profiles for Peak Shaving Applications

“…However, it is difficult to take the profile's differences into account based on the European distance [6]. ere are a lot of clustering methods including hierarchical clustering [7], density-based spatial clustering [8], fuzzy clustering [9], mean shift clustering [10], and so on. Although these algorithms can improve the efficiency and quality of clustering to an extent, the distance-based method can only describe the profile's characteristics from the overall or macroscopic level.…”