Data clustering algorithms are used in many fields like anonymisation of databases, image processing, analysis of satellite images and medical data analysis. There are several C-Means clustering algorithms in the literature. Besides the hard C-Means, there are uncertainty based C-Means algorithms like the Fuzzy C-Means algorithm and its variants, the Rough C-Means algorithm, the Intuitionistic Fuzzy C- Means algorithm and the hybrid C-Means algorithms (Rough Fuzzy C-Means algorithm). In this paper we propose a new hybrid clustering algorithm called Rough Intuitionistic Fuzzy C-Means and evaluate its performance in comparison to the other algorithms mentioned above. We have applied these algorithms on numerical as well as image data of two different types and used some benchmarking indexes for the evaluation of their performance. The results show that the proposed algorithm outperforms the existing algorithms in almost all cases. © 2013 ACM.

Balakrushna Tripathy

Department of Analytics

School of Computer Science and Engineering

Vellore Campus

Swarnalatha P

Department of Information Security

Bhargava R

Tripathy A

Dhull R

Verma E

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Rough intuitionistic fuzzy C-means algorithm and a comparative analysis

Proceedings of the 6th ACM India Computing Convention on - Compute '13

Rough Intuitionistic Fuzzy C-Means Algorithm is a combination of Fuzzy Sets, Rough Sets and Intuitionistic Fuzzy Sets. This algorithm provides high quality clustering over numeric datasets. However, RIFCM is highly inconsistent over Image datasets. In this paper, we combine RIFCM with Firefly Algorithm. Firefly algorithm is a meta-heuristic bio-inspired algorithm which mimics the behavior of fireflies. Our experimental results prove that using Firefly algorithm before RIFCM lends stability to the clustering output and considerably reduces the number of iterations required for convergence. © Springer International Publishing AG 2018.

Information and Communication Technology for Intelligent Systems (ICTIS 2017) - Volume 2 Smart Innovation, Systems and Technologies

Stabilizing Rough Sets Based Clustering Algorithms Using Firefly Algorithm over Image Datasets

Hybrid Intelligence for Image Analysis and Understanding

Uncertainty-Based Clustering Algorithms for Medical Image Analysis

Over the years data clustering algorithms have been used for image segmentation. Due to the presence of uncertainty in real life datasets, several uncertainty based data clustering algorithms have been developed. The c-means clustering algorithms form one such family of algorithms. Starting with the fuzzy c-means (FCM) a subfamily of this family comprises of rough c-means (RCM), intuitionistic fuzzy c-means (IFCM) and their hybrids like rough fuzzy c-means (RFCM) and rough intuitionistic fuzzy c-means (RIFCM). In the basic subfamily of this family of algorithms, the Euclidean distance was being used to measure the similarity of data. However, the sub family of algorithms obtained replacing the Euclidean distance by kernel based similarities produced better results. Especially, these algorithms were useful in handling viably cluster data points which are linearly inseparable in original input space. During this period it was inferred by Krishnapuram and Keller that the membership constraints in some rudimentary uncertainty based clustering techniques like fuzzy c-means imparts them a probabilistic nature, hence they suggested its possibilistic version. In fact all the other member algorithms from basic subfamily have been extended to incorporate this new notion. Currently, the use of image data is growing vigorously and constantly, accounting to huge figures leading to big data. Moreover, since image segmentation happens to be one of the most time consuming processes, industries are in the need of algorithms which can solve this problem at a rapid pace and with high accuracy. In this paper, we propose to combine the notions of kernel and possibilistic approach together in a distributed environment provided by Apache™ Hadoop. We integrate this combined notion with map-reduce paradigm of Hadoop and put forth three novel algorithms; Hadoop based possibilistic kernelized rough c-means (HPKRCM), Hadoop based possibilistic kernelized rough fuzzy c-means (HPKRFCM) and Hadoop based possibilistic kernelized rough intuitionistic fuzzy c-means (HPKRIFCM) and study their efficiency in image segmentation. We compare their running times and analyze their efficiencies with the corresponding algorithms from the other three sub families on four different types of images, three different kernels and six different efficiency measures; the Davis Bouldin index (DB), Dunn index (D), alpha index (α), rho index (ρ), alpha star index (α*) and gamma index (γ). Our analysis shows that the hyper-tangent kernel with Hadoop based possibilistic kernelized rough intuitionistic fuzzy c-means is the best one for image segmentation among all these clustering algorithms. Also, the times taken to render segmented images by the proposed algorithms are drastically low in comparison to the other algorithms. The implementations of the algorithms have been carried out in Java and for the proposed algorithms we have used Hadoop framework installed on CentOS. For statistical plotting we have used matplotlib (python library). © 2016 Elsevier B.V.

Applied Soft Computing

Hadoop based uncertain possibilistic kernelized c-means algorithms for image segmentation and a comparative analysis

Data clustering has been an integral and important part of data mining. It has wide applications in database anonymization, decision making, image processing and pattern recognition, medical diagnosis, and geographical information systems, only to name a few. Data in real-life scenario are having imprecision inherent in them. So, early crisp clustering techniques are very less efficient. Several imprecisionbased models have been proposed over the years. Of late, it has been established that the hybrid models obtained as combination of these imprecise models are far more efficient than the individual ones. Several clustering algorithms have been put forth using these hybrid models. It is also found that conventional fuzzy clustering algorithms fail in incorporating the spatial information. This chapter focuses on discussing some of the spatial data clustering algorithms developed so far and their applications mainly in the area of image segmentation. © Springer International Publishing AG 2016.

Hybrid Soft Computing for Image Segmentation

Uncertainty-Based Spatial Data Clustering Algorithms for Image Segmentation

Application of clustering algorithms for investigating real life data has concerned many researchers and vague approaches or their hybridization with other analogous approaches has gained special attention due to their great effectiveness. Recently, rough intuitionistic fuzzy c-means algorithm has been proposed by Tripathy et al [3] and they established its supremacy over all other algorithms contained in the same set. Replacing the Euclidean distance metric with kernel induced metric makes it possible to cluster the objects which are linearly inseparable in the original space. In this paper a comparative analysis is performed over the Gaussian, hyper tangent and radial basis kernel functions by their application on various vague clustering approaches like rough c-means (RCM), intuitionistic fuzzy c-means (IFCM), rough fuzzy c-means (RFCM) and rough intuitionistic fuzzy c-means (RIFCM). All clustering algorithms have been tested on synthetic, user knowledge modeling and human activity recognition datasets taken from UCI repository against the standard accuracy indexes for clustering. The results reveal that for small sized datasets Gaussian kernel produces more accurate clustering than radial basis and hyper tangent kernel functions however for the datasets which are considerably large hyper tangent kernel is superior to other kernel functions. All experiments have been carried out using C language and python libraries have been used for statistical plotting. © 2015 IEEE.

2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI)

Efficiency analysis of kernel functions in uncertainty based c-means algorithms

Fuzzy Set and Its Extension

Fuzzy/Intuitionistic Fuzzy Set Theory

Fundamenta Informaticae

Rough-Fuzzy Circular Clustering for Color Normalization of Histological Images

2011 IEEE International Conference on Bioinformatics and Biomedicine

Microarray Time-Series Data Clustering Using Rough-Fuzzy C-Means Algorithm

A novel intuitionistic fuzzy C means clustering algorithm and its application to medical images

IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)

Rough Set Based Generalized Fuzzy $C$ -Means Algorithm and Quantitative Indices

Journal	Data powered by TypesetProceedings of the 6th ACM India Computing Convention on - Compute '13
Publisher	Data powered by TypesetACM Press
Open Access	0