Robust Intuitionistic Fuzzy C-means clustering for linearly and nonlinearly separable data

Kaur, Prabhjot; Soni, Ajay; Gosain, Anjana

doi:10.1109/iciip.2011.6108908

Cited by 23 publications

(8 citation statements)

References 11 publications

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“…This algorithm modified the existing FCM algorithm and was proposed as EFCM 16 . In this, an additional method of image enhancement was incorporated before implementing the FCM algorithm.…”

Section: Background Informationmentioning

confidence: 99%

“…KFCM 17 worked efficiently with nonlinear data set but it is still sensitive to noise and to prevent that CKFCM algorithm was developed. Credibility parameter is introduced into the objective function of KFCM, which assigned lower membership to those points that are far away from the center of the cluster.…”

Section: Background Informationmentioning

confidence: 99%

“…Its computational time is very less as it did not add any complex equations within the objective function of FCM. In 2012, the intuitionistic version of FCM σ (IFCM σ ) 16,17 and KFCM σ (KIFCM σ ) 16,17 is proposed by P. Kaur et al In order to remove KFCM's noise sensitivity, the kernel‐based CFCM (CKFCM) algorithm 18 was implemented in 2016, which used credibility term to reduce the sensitivity of the noisy points. In the same year, R. Shang proposed clone kernel spatial FCM (CKS FCM) 19 to resolve the issue of noise sensitivity and reliance of clustering results on the initial cluster centers.…”

Section: Introductionmentioning

confidence: 99%

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Fuzzy clustering‐based image segmentation techniques used to segment magnetic resonance imaging/computed tomography scan brain tissues: Comparative analysis

Kaur

Sharma

Palmia

2020

Int J Imaging Syst Tech

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Medical images are obtained with computer‐aided diagnosis using electronic devices such as CT scanners and MRI machines. The captured computed tomography (CT)/magnetic resonance imaging (MRI) images typically have limited spatial resolution, low contrast, noise and nonuniform variability in intensity due to environmental effects. Therefore, the distinctions of the objects are blurred, distorted and the meanings of the objects are not quite precise. Fuzzy sets and fuzzy logic are best suited for addressing vagueness and ambiguity. Fuzzy clustering technique has been commonly used for segmentation of images throughout the last decade. This study presents a comparative study of 14 fuzzy‐clustered image segmentation algorithms used in the CT scan and MRI brain image segments. This study used 17 data sets including 4 synthetic data sets, namely, Bensaid, Diamond, Square, and its noisy version, 5 real‐world digital images, and 8 CT scan/MRI brain images to analyze the algorithms. Ground truth images are used for qualitative analysis. Apart from the qualitative analysis, the study also quantitatively evaluated the methods using three validity metrics, namely, partition coefficient, partition entropy, and Fukuyama‐Sugeno. After a thorough and careful review of the results, it is observed that extension of the fuzzy C‐means (EFCM) outperformed every other image segmentation algorithm, even in a noisy environment, followed by kernel‐based FCM σ, the output of which is also very good after EFCM.

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“…This algorithm modified the existing FCM algorithm and was proposed as EFCM 16 . In this, an additional method of image enhancement was incorporated before implementing the FCM algorithm.…”

Section: Background Informationmentioning

confidence: 99%

Section: Background Informationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Fuzzy clustering‐based image segmentation techniques used to segment magnetic resonance imaging/computed tomography scan brain tissues: Comparative analysis

Kaur

Sharma

Palmia

2020

Int J Imaging Syst Tech

Self Cite

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“…Son et al (2012aSon et al ( , b, 2013Son et al ( , 2014 and Son (2014aSon ( , b, c, 2015 proposed intuitionistic fuzzy clustering algorithms for geodemographic analysis based on recent results regarding IFS and the possibilistic FCM. Kernel-based fuzzy clustering (KFCM) was applied to enhance the clustering quality of FCM such as in Graves and Pedrycz (2010), Kaur et al (2012) and Lin (2014). Summaries of the recent intuitionistic fuzzy clustering are referenced in (Xu 2012).…”

Section: Introductionmentioning

confidence: 99%

Picture fuzzy clustering: a new computational intelligence method

2015

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Fuzzy clustering especially fuzzy C-means (FCM) is considered as a useful tool in the processes of pattern recognition and knowledge discovery from a database; thus being applied to various crucial, socioeconomic applications. Nevertheless, the clustering quality of FCM is not high since this algorithm is deployed on the basis of the traditional fuzzy sets, which have some limitations in the membership representation, the determination of hesitancy and the vagueness of prototype parameters. Various improvement versions of FCM on some extensions of the traditional fuzzy sets have been proposed to tackle with those limitations. In this paper, we consider another improvement of FCM on the picture fuzzy sets, which is a generalization of the traditional fuzzy sets and the intuitionistic fuzzy sets, and present a novel picture fuzzy clustering algorithm, the so-called FC-PFS. A numerical example on the IRIS dataset is conducted to illustrate the activities of the proposed algorithm. The experimental results on various benchmark datasets of UCI Machine Learning Repository under different scenarios of parameters of the algorithm reveal that FC-PFS has better Communicated by V. Loia.

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“…Prabhjot kaur et al [20] presented a robust IFCM and kernel version of IFCM with a new distance metric incorporating the distance variation of data-points within each cluster. Rohan Bhargava et al [21] hybridized rough set with IFS in order to describe a cluster by its centroid and its lower and upper approximations.…”

Section: Literature Surveymentioning

confidence: 99%

An Application of Pso-Based Intuitionistic Fuzzy Clustering to Medical Datasets

Parvathavarthini¹,

Visalakshi²,

Shanthi³

et al. 2017

IJSC

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Clustering is the process of splitting data into several groups based on the characteristics of data. Fuzzy clustering assigns a data object to various clusters based on different membership values. In medical field, the diagnosis of the disease has to be done without faults and in an earlier time without any delay. So, there is a need to represent imprecise nature of the data. To represent vague data in a clear manner, Intuitionistic fuzzy set introduces a parameter called hesitancy degree. In case of Intuitionistic fuzzy clustering, this indicates that the user is not aware whether the object belongs to or not belongs to a cluster. In such a case, hesitancy can very well represent the inherent noise in the data or the ignorance of the user that is given by the state 'may be'. All clustering algorithms choose the initial seed in a random fashion. But, this creates a serious impact on the convergence of the algorithm and the clustering algorithms tend to fall into local minima. This work utilizes Intuitionistic fuzzy Particle Swarm Optimization to initialize the centroids for the Intuitionistic fuzzy clustering algorithm. The algorithm is executed over medical datasets from UCI repository and the results indicate that optimal clusters are achieved. The proposed method performs well when compared with IFCM and FCM-PSO.

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Robust Intuitionistic Fuzzy C-means clustering for linearly and nonlinearly separable data

Cited by 23 publications

References 11 publications

Fuzzy clustering‐based image segmentation techniques used to segment magnetic resonance imaging/computed tomography scan brain tissues: Comparative analysis

Fuzzy clustering‐based image segmentation techniques used to segment magnetic resonance imaging/computed tomography scan brain tissues: Comparative analysis

Picture fuzzy clustering: a new computational intelligence method

An Application of Pso-Based Intuitionistic Fuzzy Clustering to Medical Datasets

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