Proceedings ArticleDOI
On PRIFCM algorithm for data clustering, image segmentation and comparative analysis
B. K. Tripathy,Anurag Tripathy,K. Govindarajulu +2 more
- pp 333-336
Reads0
Chats0
TLDR
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.read more
Citations
More filters
Journal ArticleDOI
Hadoop based uncertain possibilistic kernelized c-means algorithms for image segmentation and a comparative analysis
B. K. Tripathy,Dishant Mittal +1 more
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.
Book ChapterDOI
A New Approach to Fuzzy Soft Set Theory and Its Application in Decision Making
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.
Proceedings ArticleDOI
A comparative review of various segmentation methods and its application
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.
Book ChapterDOI
Application of Rough Set Based Models in Medical Diagnosis
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.
Book ChapterDOI
Uncertainty-Based Clustering Algorithms for Large Data Sets
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.
References
More filters
Journal ArticleDOI
Rough sets
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.
Journal ArticleDOI
A possibilistic approach to clustering
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.
Journal ArticleDOI
RFCM: A Hybrid Clustering Algorithm Using Rough and Fuzzy Sets
Pradipta Maji,Sankar K. Pal +1 more
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.
Proceedings ArticleDOI
Image segmentation using spatial intuitionistic fuzzy C means clustering
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.
Proceedings ArticleDOI
Rough intuitionistic fuzzy C-means algorithm and a comparative analysis
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.