Proceedings ArticleDOI
A refined rough fuzzy clustering algorithm
Sahil Sobti,Vivek Shah,B. K. Tripathy +2 more
- pp 1-4
TLDR
A rough fuzzy C-Means (RFCM) algorithm is generated using both numeric as well as image datasets as input and use the performance indices DB and D for this purpose.Abstract:
Clustering is a familiar concept in the realm of Data mining and has wide applications in areas like image processing, pattern recognition and rule generation. Uncertainty in present day databases is a common feature. In order to handle these datasets, several clustering algorithms have been formulated in the literature. The first one being the Fuzzy C-Means (FCM) algorithm and it was followed by the Rough C-Means (RCM) by Lingras. In the paper Lingras has refined his previous algorithm. We combine this algorithm with the fuzzy C-means algorithm to generate a rough fuzzy C-Means (RFCM) algorithm in this paper. Also, we provide a comparative analysis with earlier RFCM algorithm introduced by Mitra et al and establish that our algorithm performs better. We use both numeric as well as image datasets as input and use the performance indices DB and D for this purpose.read more
Citations
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IEEE transactions on pattern analysis and machine intelligence
TL;DR: This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis and addressing interesting real-world computer Vision and multimedia applications.
Journal ArticleDOI
Robust fuzzy clustering algorithms integrating membership guided image filtering
Journal ArticleDOI
Kernelised Rough Sets Based Clustering Algorithms Fused With Firefly Algorithm for Image Segmentation
TL;DR: This paper describes the design and construction of the proposed Algorithm for Firefly’s rough-tangent-Kernel system.
Book ChapterDOI
Leukemia Cell Segmentation from Microscopic Blood Smear Image Using C-Mode
Neha Singh,B. K. Tripathy +1 more
TL;DR: Improving the present method by application of soft computing to provide accuracy is the focus of the paper and can be achieved by applying C-mode on RGB image, this removes number of steps which was earlier needed to process.
References
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Fuzzy sets
TL;DR: A separation theorem for convex fuzzy sets is proved without requiring that the fuzzy sets be disjoint.
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Pattern Recognition with Fuzzy Objective Function Algorithms
TL;DR: Books, as a source that may involve the facts, opinion, literature, religion, and many others are the great friends to join with, becomes what you need to get.
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TL;DR: Various properties are proved, which are connected to the operations and relations over sets, and with modal and topological operators, defined over the set of IFS's.
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Fuzzy Sets and Fuzzy Logic: Theory and Applications
George J. Klir,Bo Yuan +1 more
TL;DR: Fuzzy Sets and Fuzzy Logic is a true magnum opus; it addresses practically every significant topic in the broad expanse of the union of fuzzy set theory and fuzzy logic.