Rough intuitionistic fuzzy C-means algorithm and a comparative analysis

Bhargava, Rohan; Tripathy, B. K.; Tripathy, Anurag; Dhull, Rajkamal; Verma, Ekta; Swarnalatha, P.

doi:10.1145/2522548.2523140

Cited by 22 publications

(7 citation statements)

References 10 publications

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“…Hybrid clustering algorithms by combining fuzzy sets, rough sets or intuitionistic fuzzy sets have been proposed and studied. Some of these algorithms are the rough fuzzy c-means (RFCM) [11,12] and the rough fuzzy intuitionistic fuzzy cmeans (RIFCM) [13]. As expected the RIFCM performs better than all the other algorithms in this family like the FCM, RCM and RFCM as established in [13] through several experiments.…”

Section: Introduction (Heading 1)mentioning

confidence: 75%

“…Some of these algorithms are the rough fuzzy c-means (RFCM) [11,12] and the rough fuzzy intuitionistic fuzzy cmeans (RIFCM) [13]. As expected the RIFCM performs better than all the other algorithms in this family like the FCM, RCM and RFCM as established in [13] through several experiments. They have used efficiency measuring indices like the Davies-Bouldin (DB) [14] and Dunn (D) indexes [15] to compare the accuracy of performance of these algorithms by using different types of images and datasets for the purpose.…”

Section: Introduction (Heading 1)mentioning

confidence: 75%

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Possibilistic rough fuzzy C-means algorithm in data clustering and image segmentation

Tripathy

Rajulu

2014

2014 IEEE International Conference on Computational Intelligence and Computing Research

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Several data clustering techniques have been developed in literature. It has been observed that the algorithms developed by using imprecise models like rough sets, fuzzy sets and intuitionistic fuzzy sets have been better than the crisp algorithms. Also, the hybrid models provide far better clustering algorithms than the individual models. Several such models have been developed by using a combination of fuzzy set introduced by Zadeh, the rough set introduced by Pawlak and the intuitionistic fuzzy introduced by Atanassov. Notable among them being the Rough Fuzzy C-Means (RFCM) introduced by Mitra et al and the rough intuitionistic fuzzy c-means algorithm (RIFCM) introduced and studied by Tripathy et al Krishnapuram and Keller observed that the basic clustering algorithms have the probabilistic flavour; for example due to the presence of the constraint on the memberships used in the fuzzy C-Means (FCM) algorithm. So, they introduced the concept of possibilistic approach and developed a possibilistic fuzzy C-means (PFCM) algorithm. Another approach to PFCM is due to Pal et al. In this paper, we improve a possibilistic rough C-Means (PRCM) algorithm introduced by Anuradha et,al. and introduce a newalgorithm, which we call as possibilistic rough fuzzy C-Means (PRFCM) and compare its efficiency with the improved PRCM and the basic PRFCM algorithm to establish experimentally that this algorithm is comparatively better than PRCM and the corresponding RCM algorithm. We perform the experimental analysis by taking different types of numerical datasets and images as inputs and using standard accuracy measures like the DB and the D-index.

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Section: Introduction (Heading 1)mentioning

confidence: 75%

Section: Introduction (Heading 1)mentioning

confidence: 75%

Possibilistic rough fuzzy C-means algorithm in data clustering and image segmentation

Tripathy

Rajulu

2014

2014 IEEE International Conference on Computational Intelligence and Computing Research

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“…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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“…Later, intuitionistic fuzzy sets [3] and rough sets were introduced [4]. Applying these models, several new clustering algorithms were developed such as Intuitionistic Fuzzy C-Means (IFCM) [5] that used the concept of intuitionistic fuzzy sets, Rough Fuzzy C-Means (RFCM) [6,7] that used the concept of rough fuzzy sets and, Rough Intuitionistic Fuzzy C-Means (RIFCM) [8], that used the concept of both, intuitionistic fuzzy sets and rough fuzzy sets.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Analysis of Firefly and FuzzyFirefly based Kernelized Hybrid C-Means Algorithms

Tripathy¹,

Agrawal²,

Reddy³

2019

IJISA

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In most of the clustering algorithms, the assignment of initial centroids is performed randomly, which affects both the final outcome and the number of iterations required. Another aspect of the approaches in clustering algorithms is the use of Euclidean distance as the measure of similarity between data points, which is handicapped by linear separability of input data. The purpose of this paper is to combine suitable techniques so that both the above problems can be handled suitably leading to efficient algorithms. For the initial assignment of centroids we use Firefly and Fuzzy Firefly algorithms. We replace the Euclidean distance by Kernels (Gaussian and Hyper-tangent) leading to hybridized versions. For experimental analysis we use five different images from different domains as input. Two efficiency measures; Davis Bouldin index (DB) and Dunn index (D) are used for comparison. The tabular values, their graphical representations and output images are generated to support the claims. The analysis proves the superiority of the optimized algorithms over their existing counterparts. We also find that Hyper-tangent kernel with Rough Intuitionistic Fuzzy C-Means algorithm using Fuzzy Firefly algorithm produces the best results and has a much faster convergence rate. The analysis of medical, satellite or geographical images can be done more efficiently using the proposed optimized algorithms. It is supposed to play an important role in image segmentation and analysis.

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Rough intuitionistic fuzzy C-means algorithm and a comparative analysis

Cited by 22 publications

References 10 publications

Possibilistic rough fuzzy C-means algorithm in data clustering and image segmentation

Possibilistic rough fuzzy C-means algorithm in data clustering and image segmentation

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

A Comparative Analysis of Firefly and FuzzyFirefly based Kernelized Hybrid C-Means Algorithms

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