A Novel Intuitionistic Fuzzy Set Generator with Application to Clustering

Self Cite

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.

Section: A Main Resultsmentioning

confidence: 99%

“…We compute the AIFS counterpart of U by three functions based generalized Yager's intuitionistic fuzzy generator [32]. Hence, the AIFSs derived against…”

Section: B Proposal Of Ifcom Clustering Algorithmmentioning

confidence: 99%

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Intuitionistic Fuzzy c-Ordered Means Clustering Algorithm

Kaushal

Lohani

2022

Self Cite

“…Definition 2.4: AIFSs generator [68] The membership (µ(x)) is a fuzzy mapping Z(x), tuning parameter α > 0 and a constant ζ ∈ [0,1] dependent function. In the non-membership function (ν(x)) and hesitancy function (π(x)) one more tuning parameter β is involved as follows:…”

Section: Definition 23: Distance Measure Between Aifss [66]mentioning

confidence: 99%

Proposed Intuitionistic Fuzzy Entropy Measure Along With Novel Multicriteria Sorting Techniques

Tiwari

Lohani

2023

Recently, the major environmental changes and a pandemic called COVID-19 have heavily impacted the economy, business, and health of each country. Moreover, the climatic changes and COVID-19 are calamities to human life. In other words, these two aspects threaten the existence of humans and the sustenance of the all-around development of a country. These two factors particularly influence the tourism sector, so a strategy balancing environmental quality and dealing with the ill effects of COVID-19 is formulated to uplift the economic sectors. Atannasov's intuitionistic fuzzy domain is used to model the environmental quality and COVID-19 due to the involvement of hesitancy and uncertainty. The precise measurement of the imprecision in the information is obtained with the help of entropy measure. The paper analyzes the two aspects using a novel entropy measure based on multiple criteria sorting (MCS). Here, the two MCS problems are solved with the help of two proposed techniques: TOPSIS-GREY-sort and ENTROPY-TOPSIS-GREY-sort. A case study showing the impact of COVID-19 in the Philippines and the environmental quality of Tehran (the capital city of Iran) are considered to validate the functioning of the proposed techniques. We use "A novel sorting method TOPSIS-SORT: an application for Tehran environmental quality evaluation (2016), Ekonomica a management", and "Current Issues in Tourism 25.2(2022): 168-178, Taylor and Francis" for the comparative analysis.INDEX TERMS Atanassov intuitionistic fuzzy set (AIFS), Multi critria sorting (MCS), Entropy, COVID-19, Environmental quality, TOPSIS, GRA.

“…It is obviously affected by local spatial information, so points at different locations have different membership values. Similarly, in the case of low contrast, the function of local information is still limited [38]. The main reason is that the homogeneity of local regions may have a negative impact on clustering, resulting in the same misclassified state throughout the region.…”

Section: A Improvement Of Global Distribution Information Based On Gmentioning

confidence: 99%

An Intuitionistic Kernel-Based Fuzzy C-Means Clustering Algorithm With Local Information for Power Equipment Image Segmentation

Chen

Wang

2020

With the transformation of the national energy and power sector, the steady advancement of intelligent power grid construction and the continuous improvement of the Ubiquitous Power Internet of Things technology framework, it has further requirements for realizing state comprehensive awareness and efficient processing of information data, and has been widely used in power equipment. The infrared image recognition technology, which has been widely used in thermal fault diagnosis of power equipment, also requires deeper research. For traditional Intuitionistic Fuzzy C-means (IFCM) algorithm for image segmentation is sensitive to the clustering center lead to low final clustering precision and detail, the time complexity and the high shortage. The paper puts forward a kind of applicable to power equipment of the infrared image segmentation based on space distribution information of Intuitionistic Fuzzy clustering algorithm. Non-target objects with high intensity and uneven image intensity in infrared image have strong interference to image segmentation. The proposed algorithm can effectively suppress the interference. Firstly, the gaussian model is introduced into the global spatial distribution information of power equipment to improve the IFCM. Secondly, the spatial operator optimization membership function of local spatial information is used to solve the problem of edge blurring and uneven image intensity. Through experiments on the data set containing 300 infrared images of power equipment, the relative regional error rate is about 10%, which is less affected by the change of fuzzy factor m. The effectiveness and applicability of this algorithm are verified, which is obviously better than other comparison algorithms. INDEX TERMS Intuitionistic fuzzy clustering, infrared image, Gaussian model, local information.