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Proceedings ArticleDOI

On PRIFCM algorithm for data clustering, image segmentation and comparative analysis

12 Jun 2015-pp 333-336
TL;DR: A possibilistic rough intuitionistic fuzzy C-Means algorithm (PRIFCM) is proposed and its efficiency is compared with other possibillistic algorithms and the RIFCM to establish the superiority of PRI FCM.
Abstract: Data clustering has been playing important roles in many areas like pattern recognition, image segmentation, social networks and database anonymisation. Since most of the data available in real life situation are imprecise by nature, many imprecision based data clustering algorithms are found in literature using individual imprecise models as well as their hybrids. It was observed by Krishnapuram and Keller that the possibilistic approach to the basic clustering algorithms is more efficient as the drawbacks of the basic algorithms are removed. This approach was used to develop the possibilistic versions of fuzzy, rough and rough fuzzy C-Means algorithms to develop their corresponding possibilistic versions. In this paper, we extend these algorithms further by proposing a possibilistic rough intuitionistic fuzzy C-Means algorithm (PRIFCM) and compare its efficiency with other possibilistic algorithms and the RIFCM. Experimental analysis is carried out by taking both numeric as well as the image data. Also, DB and the D indices are used for the comparison which establishes the superiority of PRIFCM.
Citations
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Journal ArticleDOI
01 Sep 2016
TL;DR: The 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.
Abstract: 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 Apacheź 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 (ź), 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).

28 citations

Book ChapterDOI
01 Jan 2016
TL;DR: An application of fuzzy soft sets in decision making is provided which substantially improve and is more realistic than the algorithm proposed earlier by Maji et al.
Abstract: Soft set theory is a new mathematical approach to vagueness introduced by Molodtsov. This is a parameterized family of subsets defined over a universal set associated with a set of parameters. In this paper, we define membership function for fuzzy soft sets. Like the soft sets, fuzzy soft set is a notion which allows fuzziness over a soft set model. So far, more than one attempt has been made to define this concept. Maji et al. defined fuzzy soft sets and several operations on them. In this paper we followed the definition of soft sets provided by Tripathy et al. through characteristic functions in 2015. Many related concepts like complement of a fuzzy soft set, null fuzzy soft set, absolute fuzzy soft set, intersection of fuzzy soft sets and union of fuzzy soft sets are redefined. We provide an application of fuzzy soft sets in decision making which substantially improve and is more realistic than the algorithm proposed earlier by Maji et al.

24 citations

Proceedings ArticleDOI
01 Sep 2017
TL;DR: The various algorithms proposed for the process and the algorithms further proposed in order to apply in the fields like medical imaging, edge detection, object tracking X-radiology and agriculture are discussed.
Abstract: Image segmentation has been a subject of interest for researchers and engineers for quite a while. Its wide applicability in different fields makes it even more popular. It is a process of dividing an image into different segments which together form the complete image. Segmentation of image results in the formation of set of contours on the basis of different properties and characteristics of the pixels forming the image like texture, intensity and colors. Different regions are separated from each other depending on these parameters. There are various methods proposed for the process of image segmentation. In this paper we discuss the various algorithms proposed for the process and the algorithms further proposed in order to apply in the fields like medical imaging, edge detection, object tracking X-radiology and agriculture.

9 citations


Cites methods from "On PRIFCM algorithm for data cluste..."

  • ...However, recently a new approach called PRIFCEM (Possibilistic Rough Intuitionistic Fuzzy C-mean algorithm) was proposed for data clustering [12]....

    [...]

Book ChapterDOI
B. K. Tripathy1
01 Jan 2016
TL;DR: This chapter discusses on several applications of rough set theory in medical diagnosis that can be utilized as a supporting tool to the medical practitioner, mainly country like India with vast rural areas and absolute shortage of physicians.
Abstract: Modeling intelligent system in the field of medical diagnosis is still a challenging work. Intelligent systems in medical diagnosis can be utilized as a supporting tool to the medical practitioner, mainly country like India with vast rural areas and absolute shortage of physicians. Intelligent systems in the field of medical diagnosis can also able to reduce cost and problems for the diagnosis like dynamic perturbations, shortage of physicians, etc. An intelligent system may be considered as an information system that provides answer to queries relating to the information stored in the Knowledge Base (KB), which is a repository of human knowledge. Rough set theory is an efficient model to capture uncertainty in data and the processing of data using rough set techniques is easy and convincing. Rule generation is an inherent component in rough set analysis. So, medical systems which have uncertainty inherent can be handled in a better way using rough sets and its variants. The objective of this chapter is to discuss on several such applications of rough set theory in medical diagnosis.

8 citations

Book ChapterDOI
01 Jan 2018
TL;DR: It is the aim in this chapter to present the uncertainty based clustering algorithms developed so far and proposes a few new algorithms which can be developed further.
Abstract: Data clustering plays a very important role in Data mining, machine learning and Image processing areas. As modern day databases have inherent uncertainties, many uncertainty-based data clustering algorithms have been developed in this direction. These algorithms are fuzzy c-means, rough c-means, intuitionistic fuzzy c-means and the means like rough fuzzy c-means, rough intuitionistic fuzzy c-means which base on hybrid models. Also, we find many variants of these algorithms which improve them in different directions like their Kernelised versions, possibilistic versions, and possibilistic Kernelised versions. However, all the above algorithms are not effective on big data for various reasons. So, researchers have been trying for the past few years to improve these algorithms in order they can be applied to cluster big data. The algorithms are relatively few in comparison to those for datasets of reasonable size. It is our aim in this chapter to present the uncertainty based clustering algorithms developed so far and proposes a few new algorithms which can be developed further. Uncertainty-Based Clustering Algorithms for Large Data Sets

7 citations

References
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Journal ArticleDOI
TL;DR: This approach seems to be of fundamental importance to artificial intelligence (AI) and cognitive sciences, especially in the areas of machine learning, knowledge acquisition, decision analysis, knowledge discovery from databases, expert systems, decision support systems, inductive reasoning, and pattern recognition.
Abstract: Rough set theory, introduced by Zdzislaw Pawlak in the early 1980s [11, 12], is a new mathematical tool to deal with vagueness and uncertainty. This approach seems to be of fundamental importance to artificial intelligence (AI) and cognitive sciences, especially in the areas of machine learning, knowledge acquisition, decision analysis, knowledge discovery from databases, expert systems, decision support systems, inductive reasoning, and pattern recognition.

7,185 citations

Journal ArticleDOI
TL;DR: An appropriate objective function whose minimum will characterize a good possibilistic partition of the data is constructed, and the membership and prototype update equations are derived from necessary conditions for minimization of the criterion function.
Abstract: The clustering problem is cast in the framework of possibility theory. The approach differs from the existing clustering methods in that the resulting partition of the data can be interpreted as a possibilistic partition, and the membership values can be interpreted as degrees of possibility of the points belonging to the classes, i.e., the compatibilities of the points with the class prototypes. An appropriate objective function whose minimum will characterize a good possibilistic partition of the data is constructed, and the membership and prototype update equations are derived from necessary conditions for minimization of the criterion function. The advantages of the resulting family of possibilistic algorithms are illustrated by several examples. >

2,388 citations

Journal ArticleDOI
TL;DR: A hybrid unsupervised learning algorithm, termed as rough-fuzzy c-means, is proposed, which comprises a judicious integration of the principles of rough sets and fuzzy sets and which enables efficient handling of overlapping partitions.
Abstract: A hybrid unsupervised learning algorithm, termed as rough-fuzzy c-means, is proposed in this paper. It comprises a judicious integration of the principles of rough sets and fuzzy sets. While the concept of lower and upper approximations of rough sets deals with uncertainty, vagueness, and incompleteness in class definition, the membership function of fuzzy sets enables efficient handling of overlapping partitions. The concept of crisp lower bound and fuzzy boundary of a class, introduced in rough-fuzzy c-means, enables efficient selection of cluster prototypes. Several quantitative indices are introduced based on rough sets for evaluating the performance of the proposed c-means algorithm. The effectiveness of the algorithm, along with a comparison with other algorithms, has been demonstrated on a set of real life data sets.

146 citations

Proceedings ArticleDOI
01 Dec 2014
TL;DR: The algorithm proposed adds an intuitionistic approach in the membership function of the existing spatial FCM (sFCM) that is comparatively less hampered by noise and performs better than existing algorithms.
Abstract: A fuzzy algorithm is presented for image segmentation of 2D gray scale images whose quality have been degraded by various kinds of noise. Traditional Fuzzy C Means (FCM) algorithm is very sensitive to noise and does not give good results. To overcome this problem, a new fuzzy c means algorithm was introduced [1] that incorporated spatial information. The spatial function is the sum of all the membership functions within the neighborhood of the pixel under consideration. The results showed that this approach was not as sensitive to noise as compared to the traditional FCM algorithm and yielded better results. The algorithm we have proposed adds an intuitionistic approach in the membership function of the existing spatial FCM (sFCM). Intuitionistic refers to the degree of hesitation that arises as a consequence of lack of information and knowledge. Proposed method is comparatively less hampered by noise and performs better than existing algorithms.

42 citations

Proceedings ArticleDOI
22 Aug 2013
TL;DR: This paper proposes a new hybrid clustering algorithm called Rough Intuitionistic Fuzzy C-Means and evaluates its performance in comparison to the other algorithms mentioned above.
Abstract: 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.

30 citations