Neutrosophic Clustering Algorithm Based On Sparse Regular Term Constraint
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Neutrosophic Clustering Algorithm Based On Sparse Regular Term Constraint
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Author : Dan Zhang
language : en
Publisher: Infinite Study
Release Date :
Neutrosophic Clustering Algorithm Based On Sparse Regular Term Constraint written by Dan Zhang and has been published by Infinite Study this book supported file pdf, txt, epub, kindle and other format this book has been release on with Mathematics categories.
Clustering algorithm is one of the important research topics in the field of machine learning. Neutrosophic clustering is the generalization of fuzzy clustering and has been applied to many fields. this paper presents a new neutrosophic clustering algorithm with the help of regularization. Firstly, the regularization term is introduced into the FC-PFS algorithm to generate sparsity, which can reduce the complexity of the algorithm on large data sets. Secondly, we propose a method to simplify the process of determining regularization parameters. Finally, experiments show that the clustering results of this algorithm on artificial data sets and real data sets are mostly better than other clustering algorithms. Our clustering algorithm is effective in most cases.
Single Valued Neutrosophic Clustering Algorithm Based On Tsallis Entropy Maximization
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Author : Qiaoyan Li
language : en
Publisher: Infinite Study
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Single Valued Neutrosophic Clustering Algorithm Based On Tsallis Entropy Maximization written by Qiaoyan Li and has been published by Infinite Study this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.
Data clustering is an important field in pattern recognition and machine learning. Fuzzy c-means is considered as a useful tool in data clustering. Neutrosophic set, which is extension of fuzzy set, has received extensive attention in solving many real life problems of uncertainty, inaccuracy, incompleteness, inconsistency and uncertainty.
Generalization Of Fuzzy C Means Based On Neutrosophic Logic
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Author : Aboul Ella HASSANIEN
language : en
Publisher: Infinite Study
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Generalization Of Fuzzy C Means Based On Neutrosophic Logic written by Aboul Ella HASSANIEN and has been published by Infinite Study this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.
This article presents a New Neutrosophic C-Means (NNCMs) method for clustering. It uses the neutrosophic logic (NL), to generalize the Fuzzy C-Means (FCM) clustering system.
A Direct Data Cluster Analysis Method Based On Neutrosophic Set Implication
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Author : Sudan Jha
language : en
Publisher: Infinite Study
Release Date : 2020-10-01
A Direct Data Cluster Analysis Method Based On Neutrosophic Set Implication written by Sudan Jha and has been published by Infinite Study this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-01 with Computers categories.
Raw data are classified using clustering techniques in a reasonable manner to create disjoint clusters. A lot of clustering algorithms based on specific parameters have been proposed to access a high volume of datasets. This paper focuses on cluster analysis based on neutrosophic set implication, i.e., a k-means algorithm with a threshold-based clustering technique. This algorithm addresses the shortcomings of the k-means clustering algorithm by overcoming the limitations of the threshold-based clustering algorithm. To evaluate the validity of the proposed method, several validity measures and validity indices are applied to the Iris dataset (from the University of California, Irvine, Machine Learning Repository) along with k-means and threshold-based clustering algorithms. The proposed method results in more segregated datasets with compacted clusters, thus achieving higher validity indices. The method also eliminates the limitations of threshold-based clustering algorithm and validates measures and respective indices along with k-means and threshold-based clustering algorithms.
T S Based Single Valued Neutrosophic Number Equivalence Matrix And Clustering Method
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Author : Jiongmei Mo
language : en
Publisher: Infinite Study
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T S Based Single Valued Neutrosophic Number Equivalence Matrix And Clustering Method written by Jiongmei Mo and has been published by Infinite Study this book supported file pdf, txt, epub, kindle and other format this book has been release on with Mathematics categories.
Fuzzy clustering is widely used in business, biology, geography, coding for the internet and more. A single-valued neutrosophic set is a generalized fuzzy set, and its clustering algorithm has attracted more and more attention. An equivalence matrix is a common tool in clustering algorithms. At present, there exist no results constructing a single-valued neutrosophic number equivalence matrix using t-norm and t-conorm.
An Effective Clustering Method Based On Data Indeterminacy In Neutrosophic Set Domain
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Author : Elyas Rashno
language : en
Publisher: Infinite Study
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An Effective Clustering Method Based On Data Indeterminacy In Neutrosophic Set Domain written by Elyas Rashno and has been published by Infinite Study this book supported file pdf, txt, epub, kindle and other format this book has been release on with Mathematics categories.
In this work, a new clustering algorithm is proposed based on neutrosophic set (NS) theory. The main contribution is to use NS to handle boundary and outlier points as challenging points of clustering methods.
Advances In Fuzzy Clustering And Its Applications
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Author : Jose Valente de Oliveira
language : en
Publisher: John Wiley & Sons
Release Date : 2007-06-13
Advances In Fuzzy Clustering And Its Applications written by Jose Valente de Oliveira and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-06-13 with Technology & Engineering categories.
A comprehensive, coherent, and in depth presentation of the state of the art in fuzzy clustering. Fuzzy clustering is now a mature and vibrant area of research with highly innovative advanced applications. Encapsulating this through presenting a careful selection of research contributions, this book addresses timely and relevant concepts and methods, whilst identifying major challenges and recent developments in the area. Split into five clear sections, Fundamentals, Visualization, Algorithms and Computational Aspects, Real-Time and Dynamic Clustering, and Applications and Case Studies, the book covers a wealth of novel, original and fully updated material, and in particular offers: a focus on the algorithmic and computational augmentations of fuzzy clustering and its effectiveness in handling high dimensional problems, distributed problem solving and uncertainty management. presentations of the important and relevant phases of cluster design, including the role of information granules, fuzzy sets in the realization of human-centricity facet of data analysis, as well as system modelling demonstrations of how the results facilitate further detailed development of models, and enhance interpretation aspects a carefully organized illustrative series of applications and case studies in which fuzzy clustering plays a pivotal role This book will be of key interest to engineers associated with fuzzy control, bioinformatics, data mining, image processing, and pattern recognition, while computer engineers, students and researchers, in most engineering disciplines, will find this an invaluable resource and research tool.
An Improved Clustering Method For Text Documents Using Neutrosophic Logic
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Author : Nadeem Akhtar
language : en
Publisher: Infinite Study
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An Improved Clustering Method For Text Documents Using Neutrosophic Logic written by Nadeem Akhtar and has been published by Infinite Study this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.
As a technique of Information Retrieval, we can consider clustering as an unsupervised learning problem in which we provide a structure to unlabeled and unknown data.
Image Segmentation
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Author : Tao Lei
language : en
Publisher: John Wiley & Sons
Release Date : 2022-10-11
Image Segmentation written by Tao Lei and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-11 with Technology & Engineering categories.
Image Segmentation Summarizes and improves new theory, methods, and applications of current image segmentation approaches, written by leaders in the field The process of image segmentation divides an image into different regions based on the characteristics of pixels, resulting in a simplified image that can be more efficiently analyzed. Image segmentation has wide applications in numerous fields ranging from industry detection and bio-medicine to intelligent transportation and architecture. Image Segmentation: Principles, Techniques, and Applications is an up-to-date collection of recent techniques and methods devoted to the field of computer vision. Covering fundamental concepts, new theories and approaches, and a variety of practical applications including medical imaging, remote sensing, fuzzy clustering, and watershed transform. In-depth chapters present innovative methods developed by the authors—such as convolutional neural networks, graph convolutional networks, deformable convolution, and model compression—to assist graduate students and researchers apply and improve image segmentation in their work. Describes basic principles of image segmentation and related mathematical methods such as clustering, neural networks, and mathematical morphology. Introduces new methods for achieving rapid and accurate image segmentation based on classic image processing and machine learning theory. Presents techniques for improved convolutional neural networks for scene segmentation, object recognition, and change detection, etc. Highlights the effect of image segmentation in various application scenarios such as traffic image analysis, medical image analysis, remote sensing applications, and material analysis, etc. Image Segmentation: Principles, Techniques, and Applications is an essential resource for undergraduate and graduate courses such as image and video processing, computer vision, and digital signal processing, as well as researchers working in computer vision and image analysis looking to improve their techniques and methods.
Region Adjacency Graph Approach For Acral Melanocytic Lesion Segmentation
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Author : Joanna Jaworek-Korjakowska
language : en
Publisher: Infinite Study
Release Date :
Region Adjacency Graph Approach For Acral Melanocytic Lesion Segmentation written by Joanna Jaworek-Korjakowska and has been published by Infinite Study this book supported file pdf, txt, epub, kindle and other format this book has been release on with Mathematics categories.
Malignant melanoma is among the fastest increasing malignancies in many countries. Due to its propensity to metastasize and lack of effective therapies for most patients with advanced disease, early detection of melanoma is a clinical imperative. In non-Caucasian populations, melanomas are frequently located in acral volar areas and their dermoscopic appearance differs from the non-acral ones. Although lesion segmentation is a natural preliminary step towards its further analysis, so far virtually no acral skin lesion segmentation method has been proposed. Our goal was to develop an effective segmentation algorithm dedicated for acral lesions.