Book ChapterDOI
Uncertainty-Based Spatial Data Clustering Algorithms for Image Segmentation
Deepthi P. Hudedagaddi,B. K. Tripathy +1 more
- pp 209-227
TLDR
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.Abstract:
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 imprecision-based 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.read more
References
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Book
Fuzzy sets
TL;DR: A separation theorem for convex fuzzy sets is proved without requiring that the fuzzy sets be disjoint.
Journal ArticleDOI
Intuitionistic fuzzy sets
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.
Proceedings ArticleDOI
A non-local algorithm for image denoising
TL;DR: A new measure, the method noise, is proposed, to evaluate and compare the performance of digital image denoising methods, and a new algorithm, the nonlocal means (NL-means), based on a nonlocal averaging of all pixels in the image is proposed.
Journal ArticleDOI
A Cluster Separation Measure
TL;DR: A measure is presented which indicates the similarity of clusters which are assumed to have a data density which is a decreasing function of distance from a vector characteristic of the cluster which can be used to infer the appropriateness of data partitions.
Journal ArticleDOI
Rough fuzzy sets and fuzzy rough sets
Didier Dubois,Henri Prade +1 more
TL;DR: It is argued that both notions of a rough set and a fuzzy set aim to different purposes, and it is more natural to try to combine the two models of uncertainty (vagueness and coarseness) rather than to have them compete on the same problems.