The RCM algorithm may lead to undesirable solutions in practice because the points close to data points being assigned are neglected which led to the development of the decision theoretic rough set (DTRS) model and the decision theoretic rough C-means algorithm (DTRCM). It was recently improved further with the introduction of decision theoretic rough fuzzy C-means (DTRFCM). Here, we present a further improved algorithm called the decision theoretic intuitionistic fuzzy rough C-means (DTRIFCM) and provide a comparative performance analysis of DTRCM, DTRFCM and DTRIFCM through experiments and efficiency measuring indices DB, D and Acc. According to DB and D indexes DTRIFCM is better than the other two, where as far as the accuracy is concerned DTRFCM is better. We have chosen the data sets Iris, Wine, WDBC and Glass from UCI repository as input for the experimental purpose. © Springer International Publishing Switzerland 2016.

Balakrushna Tripathy

Department of Analytics

School of Computer Science and Engineering

Vellore Campus

Agrawal S

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

Decision Theoretic Rough Intuitionistic Fuzzy C-Means Algorithm

Proceedings of First International Conference on Information and Communication Technology for Intelligent Systems: Volume 1 Smart Innovation, Systems and Technologies

The decision theoretic rough set model was introduced in the 90’s in order to loosen the restrictions of conventional rough approximations. Following this the conventional rough c-means was extended to the decision theoretic rough set context by Li et al. 2014. However, the Euclidean distance used as the similarity measure in this paper had the property of separability and to rectify this problem Kernel measures were used to develop the Kernel based decision theoretic rough C-means by Ryan et al. in 2016. As it is known, the hybrid models are more efficient than the individual models this approach was further extended and the Kernel based decision theoretic rough Fuzzy C-means was introduced by them recently in 2016. As a model of uncertainty intuitionistic fuzzy sets are more general than the fuzzy sets, So, we use the intuitionistic fuzzy sets instead of fuzzy sets and introduce a Kernel based Decision theoretic rough intuitionistic Fuzzy C-means in this paper. To provide variety and measure the effects, we have selected three of the most popular kernels; the Radial Basis, the Gaussian and the hyperbolic tangent kernels in our model. For the experimentation purpose we use three datasets namely the Iris, the wine and the glass data sets from the UCI repository. The efficiency measuring indices DB and D are used for evaluating the relative efficiencies of this algorithm and the other algorithms in this direction.Our results show that the proposed model provides improved results than the other two models. Some diagrams are presented to show the results visually

Advances in Intelligent Systems and Computing Proceedings of Sixth International Conference on Soft Computing for Problem Solving

An Analysis of Decision Theoretic Kernalized Rough Intuitionistic Fuzzy C-Means

There are several algorithms used for data clustering and as imprecision has become an inherent part of datasets now days, many such algorithms have been developed so far using fuzzy sets, rough sets, intuitionistic fuzzy sets, and their hybrid models. In order to increase the flexibility of conventional rough approximations, a probability based rough sets concept was introduced in the 90s namely decision theoretic rough sets (DTRS). Using this model Li et al. extended the conventional rough c-means. Euclidean distance has been used to measure the similarity among data. As has been observed the Euclidean distance has the property of separability. So, as a solution to that several Kernel distances are used in literature. In fact, we have selected three of the most popular kernels and developed an improved Kernelized rough c-means algorithm. We compare the results with the basic decision theoretic rough c-means. For the comparison we have used three datasets namely Iris, Wine and Glass. The three Kernel functions used are the Radial Basis, the Gaussian, and the hyperbolic tangent. The experimental analysis by using the measuring indices DB and D show improved results for the Kernelized means. We also present various graphs to showcase the clustered data. © Springer Nature Singapore Pte Ltd. 2017.

Journal	Data powered by TypesetProceedings of First International Conference on Information and Communication Technology for Intelligent Systems: Volume 1 Smart Innovation, Systems and Technologies
Publisher	Data powered by TypesetSpringer International Publishing
ISSN	2190-3018
Open Access	0