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Book ChapterDOI

Fuzzy Rough Entropy Clustering Algorithm Parametrization

TL;DR: Present research concentrates on new rough entropy clustering algorithm Fuzzy RECA Rough Entropy Clustering Algorithm and extension relative to distance threshold and fuzzy threshold, namely its parameters having impact on rough entropy calculation.
Abstract: Image processing represents active research area that requires advanced and sophisticated methods capable of handling novel emerging imagery technologies. Adequate and precise capture and image interpretation is primarily based on proper image segmentation. Advances in correct image partitioning are still embracing new research areas such as fuzzy sets, rough sets and rough fuzzy sets. Present research concentrates on new rough entropy clustering algorithm Fuzzy RECA Rough Entropy Clustering Algorithm and extension relative to distance threshold and fuzzy threshold, namely its parameters having impact on rough entropy calculation. Different rough entropy measures are calculated and incorporated into Fuzzy RECA based clustering algorithm on satellite image data set. Presented results suggest that proposed fuzzy thresholds capture properly image properties and it is possible to take advantage of these characteristics in real image processing applications.
Citations
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Proceedings ArticleDOI
01 Dec 2011
TL;DR: The windowed aggregation method is designed to solve the problem of over-segmentation, which occurs in quadtree-based segmentation, in pixel-based remote sensing image classification.
Abstract: In pixel-based remote sensing image classification, the long processing time limits application of classification Image segmentation is adopted to accelerate the classification speed Image segmentation is a procedure of dividing an image into separated homogenous regions These regions are considered as objects to be classified A refined quadtree-based segmentation algorithm is proposed in the paper The windowed aggregation method is designed to solve the problem of over-segmentation, which occurs in quadtree-based segmentation A spot 5 remote sensing image in Qingdao was selected as the test image Three experiments were implemented on the test image: the first is pixel-based classification; the second is quadtree-based classification; the third is refined quadtree-based classification The pixel-based classification obtains the highest accuracy while takes more time The refined quadtree-based classification is superior to quadtree-based classification in time consumed and accuracy

9 citations


Cites methods from "Fuzzy Rough Entropy Clustering Algo..."

  • ...In recent years, many methods based on fuzzy mathematics are proposed to select threshold (4,5)....

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References
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Journal ArticleDOI
TL;DR: The basic concepts of rough set theory are presented and some rough set-based research directions and applications are pointed out, indicating that the rough set approach is fundamentally important in artificial intelligence and cognitive sciences.

2,004 citations


"Fuzzy Rough Entropy Clustering Algo..." refers background in this paper

  • ...In this way, the value of the roughness of the set X equal 0 means that X is crisp with respect to B, and conversely if R(ASB, X) > 0t henX is rough (i.e., X is vague with respect to B). Detailed information on rough set theory is provided in [ 4 , 5, 6]....

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BookDOI
09 Sep 2008
TL;DR: The Handbook of Granular Computing offers a comprehensive reference source for the granular computing community, edited by and with contributions from leading experts in the field, and represents a significant and valuable contribution to the literature.
Abstract: Although the notion is a relatively recent one, the notions and principles of Granular Computing (GrC) have appeared in a different guise in many related fields including granularity in Artificial Intelligence, interval computing, cluster analysis, quotient space theory and many others. Recent years have witnessed a renewed and expanding interest in the topic as it begins to play a key role in bioinformatics, e-commerce, machine learning, security, data mining and wireless mobile computing when it comes to the issues of effectiveness, robustness and uncertainty. The Handbook of Granular Computing offers a comprehensive reference source for the granular computing community, edited by and with contributions from leading experts in the field. Includes chapters covering the foundations of granular computing, interval analysis and fuzzy set theory; hybrid methods and models of granular computing; and applications and case studies. Divided into 5 sections: Preliminaries, Fundamentals, Methodology and Algorithms, Development of Hybrid Models and Applications and Case Studies. Presents the flow of ideas in a systematic, well-organized manner, starting with the concepts and motivation and proceeding to detailed design that materializes in specific algorithms, applications and case studies. Provides the reader with a self-contained reference that includes all pre-requisite knowledge, augmented with step-by-step explanations of more advanced concepts. The Handbook of Granular Computing represents a significant and valuable contribution to the literature and will appeal to a broad audience including researchers, students and practitioners in the fields of Computational Intelligence, pattern recognition, fuzzy sets and neural networks, system modelling, operations research and bioinformatics.

543 citations

Journal ArticleDOI
TL;DR: Methods of selecting the appropriate granule size and efficient computation of rough entropy are described, which results in minimization of roughness in both object and background regions; thereby determining the threshold of partitioning.

174 citations


"Fuzzy Rough Entropy Clustering Algo..." refers methods in this paper

  • ...Rough entropy framework in image segmentation domain has been proposed in [ 3 ] and further extended in [2] as fully robust clustering algorithmic model....

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Book
26 Sep 2008
TL;DR: In the book, the author underlines the importance of approximation spaces in searching for relevant patterns and other granules on different levels of modeling for compound concept approximations.
Abstract: The book "Rough-Granular Computing in Knowledge Discovery and Data Mining" written by Professor Jaroslaw Stepaniuk is dedicated to methods based on a combination of the following three closely related and rapidly growing areas: granular computing, rough sets, and knowledge discovery and data mining (KDD). In the book, the KDD foundations based on the rough set approach and granular computing are discussed together with illustrative applications. In searching for relevant patterns or in inducing (constructing) classifiers in KDD, different kinds of granules are modeled. In this modeling process, granules called approximation spaces play a special rule. Approximation spaces are defined by neighborhoods of objects and measures between sets of objects. In the book, the author underlines the importance of approximation spaces in searching for relevant patterns and other granules on different levels of modeling for compound concept approximations. Calculi on such granules are used for modeling computations on granules in searching for target (sub) optimal granules and their interactions on different levels of hierarchical modeling. The methods based on the combination of granular computing, the rough and fuzzy set approaches allow for an efficient construction of the high quality approximation of compound concepts.

127 citations