A Direct Data Cluster Analysis Method Based On Neutrosophic Set Implication
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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.
An Effective Clustering Method Based On Data Indeterminacy In Neutrosophic Set Domain
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Author : Elyas Rashno
language : en
Publisher: Infinite Study
Release Date :
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.
An Effective Clustering Method Based On Data Indeterminacy In Neutrosophic Set Domain
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Author : Elyas Rashnoa
language : en
Publisher: Infinite Study
Release Date :
An Effective Clustering Method Based On Data Indeterminacy In Neutrosophic Set Domain written by Elyas Rashnoa 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. In the first step, a new de nition of data indeterminacy (indeterminacy set) is proposed in NS domain based on density properties of data.
Collected Papers Volume Viii
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Author : Florentin Smarandache
language : en
Publisher: Infinite Study
Release Date : 2022-04-01
Collected Papers Volume Viii written by Florentin Smarandache and has been published by Infinite Study this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-04-01 with Mathematics categories.
This eighth volume of Collected Papers includes 75 papers comprising 973 pages on (theoretic and applied) neutrosophics, written between 2010-2022 by the author alone or in collaboration with the following 102 co-authors (alphabetically ordered) from 24 countries: Mohamed Abdel-Basset, Abduallah Gamal, Firoz Ahmad, Ahmad Yusuf Adhami, Ahmed B. Al-Nafee, Ali Hassan, Mumtaz Ali, Akbar Rezaei, Assia Bakali, Ayoub Bahnasse, Azeddine Elhassouny, Durga Banerjee, Romualdas Bausys, Mircea Boșcoianu, Traian Alexandru Buda, Bui Cong Cuong, Emilia Calefariu, Ahmet Çevik, Chang Su Kim, Victor Christianto, Dae Wan Kim, Daud Ahmad, Arindam Dey, Partha Pratim Dey, Mamouni Dhar, H. A. Elagamy, Ahmed K. Essa, Sudipta Gayen, Bibhas C. Giri, Daniela Gîfu, Noel Batista Hernández, Hojjatollah Farahani, Huda E. Khalid, Irfan Deli, Saeid Jafari, Tèmítópé Gbóláhàn Jaíyéolá, Sripati Jha, Sudan Jha, Ilanthenral Kandasamy, W.B. Vasantha Kandasamy, Darjan Karabašević, M. Karthika, Kawther F. Alhasan, Giruta Kazakeviciute-Januskeviciene, Qaisar Khan, Kishore Kumar P K, Prem Kumar Singh, Ranjan Kumar, Maikel Leyva-Vázquez, Mahmoud Ismail, Tahir Mahmood, Hafsa Masood Malik, Mohammad Abobala, Mai Mohamed, Gunasekaran Manogaran, Seema Mehra, Kalyan Mondal, Mohamed Talea, Mullai Murugappan, Muhammad Akram, Muhammad Aslam Malik, Muhammad Khalid Mahmood, Nivetha Martin, Durga Nagarajan, Nguyen Van Dinh, Nguyen Xuan Thao, Lewis Nkenyereya, Jagan M. Obbineni, M. Parimala, S. K. Patro, Peide Liu, Pham Hong Phong, Surapati Pramanik, Gyanendra Prasad Joshi, Quek Shio Gai, R. Radha, A.A. Salama, S. Satham Hussain, Mehmet Șahin, Said Broumi, Ganeshsree Selvachandran, Selvaraj Ganesan, Shahbaz Ali, Shouzhen Zeng, Manjeet Singh, A. Stanis Arul Mary, Dragiša Stanujkić, Yusuf Șubaș, Rui-Pu Tan, Mirela Teodorescu, Selçuk Topal, Zenonas Turskis, Vakkas Uluçay, Norberto Valcárcel Izquierdo, V. Venkateswara Rao, Volkan Duran, Ying Li, Young Bae Jun, Wadei F. Al-Omeri, Jian-qiang Wang, Lihshing Leigh Wang, Edmundas Kazimieras Zavadskas.
Neutrosophic Clustering Algorithm Based On Sparse Regular Term Constraint
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Author : Dan Zhang
language : en
Publisher: Infinite Study
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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.
Informational Paradigm Management Of Uncertainty And Theoretical Formalisms In The Clustering Framework A Review
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Author : Pierpaolo D’Urso
language : en
Publisher: Infinite Study
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Informational Paradigm Management Of Uncertainty And Theoretical Formalisms In The Clustering Framework A Review written by Pierpaolo D’Urso 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.
Fifty years have gone by since the publication of the first paper on clustering based on fuzzy sets theory.
Fuzzy Equivalence On Standard And Rough Neutrosophic Sets And Applications To Clustering Analysis
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Author : Nguyen Xuan Thao
language : en
Publisher: Infinite Study
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Fuzzy Equivalence On Standard And Rough Neutrosophic Sets And Applications To Clustering Analysis written by Nguyen Xuan Thao 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.
In this paper, we propose the concept of fuzzy equivalence on standard neutrosophic sets and rough standard neutrosophic sets. We also provide some formulas for fuzzy equivalence on standard neutrosophic sets and rough standard neutrosophic sets. We also apply these formulas for cluster analysis. Numerical examples are illustrated.
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.
Cluster Analysis For Data Mining And System Identification
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Author : János Abonyi
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-08-10
Cluster Analysis For Data Mining And System Identification written by János Abonyi and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-08-10 with Mathematics categories.
The aim of this book is to illustrate that advanced fuzzy clustering algorithms can be used not only for partitioning of the data. It can also be used for visualization, regression, classification and time-series analysis, hence fuzzy cluster analysis is a good approach to solve complex data mining and system identification problems. This book is oriented to undergraduate and postgraduate and is well suited for teaching purposes.
An Introduction To Clustering With R
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Author : Paolo Giordani
language : en
Publisher: Springer Nature
Release Date : 2020-08-27
An Introduction To Clustering With R written by Paolo Giordani and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-08-27 with Mathematics categories.
The purpose of this book is to thoroughly prepare the reader for applied research in clustering. Cluster analysis comprises a class of statistical techniques for classifying multivariate data into groups or clusters based on their similar features. Clustering is nowadays widely used in several domains of research, such as social sciences, psychology, and marketing, highlighting its multidisciplinary nature. This book provides an accessible and comprehensive introduction to clustering and offers practical guidelines for applying clustering tools by carefully chosen real-life datasets and extensive data analyses. The procedures addressed in this book include traditional hard clustering methods and up-to-date developments in soft clustering. Attention is paid to practical examples and applications through the open source statistical software R. Commented R code and output for conducting, step by step, complete cluster analyses are available. The book is intended for researchers interested in applying clustering methods. Basic notions on theoretical issues and on R are provided so that professionals as well as novices with little or no background in the subject will benefit from the book.