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Partitional Clustering Via Nonsmooth Optimization
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Book Synopsis Partitional Clustering via Nonsmooth Optimization by : Adil M. Bagirov
Download or read book Partitional Clustering via Nonsmooth Optimization written by Adil M. Bagirov and published by Springer Nature. This book was released on 2020-02-24 with total page 343 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes optimization models of clustering problems and clustering algorithms based on optimization techniques, including their implementation, evaluation, and applications. The book gives a comprehensive and detailed description of optimization approaches for solving clustering problems; the authors' emphasis on clustering algorithms is based on deterministic methods of optimization. The book also includes results on real-time clustering algorithms based on optimization techniques, addresses implementation issues of these clustering algorithms, and discusses new challenges arising from big data. The book is ideal for anyone teaching or learning clustering algorithms. It provides an accessible introduction to the field and it is well suited for practitioners already familiar with the basics of optimization.
Book Synopsis Partitional Clustering Algorithms by : M. Emre Celebi
Download or read book Partitional Clustering Algorithms written by M. Emre Celebi and published by Springer. This book was released on 2014-11-07 with total page 420 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on partitional clustering algorithms, which are commonly used in engineering and computer scientific applications. The goal of this volume is to summarize the state-of-the-art in partitional clustering. The book includes such topics as center-based clustering, competitive learning clustering and density-based clustering. Each chapter is contributed by a leading expert in the field.
Book Synopsis Nonsmooth Optimization in Honor of the 60th Birthday of Adil M. Bagirov by : Napsu Karmitsa
Download or read book Nonsmooth Optimization in Honor of the 60th Birthday of Adil M. Bagirov written by Napsu Karmitsa and published by MDPI. This book was released on 2020-12-18 with total page 116 pages. Available in PDF, EPUB and Kindle. Book excerpt: The aim of this book was to collect the most recent methods developed for NSO and its practical applications. The book contains seven papers: The first is the foreword by the Guest Editors giving a brief review of NSO and its real-life applications and acknowledging the outstanding contributions of Professor Adil Bagirov to both the theoretical and practical aspects of NSO. The second paper introduces a new and very efficient algorithm for solving uncertain unit-commitment (UC) problems. The third paper proposes a new nonsmooth version of the generalized damped Gauss–Newton method for solving nonlinear complementarity problems. In the fourth paper, the abs-linear representation of piecewise linear functions is extended to yield simultaneously their DC decomposition as well as the pair of generalized gradients. The fifth paper presents the use of biased-randomized algorithms as an effective methodology to cope with NP-hard and nonsmooth optimization problems in many practical applications. In the sixth paper, a problem concerning the scheduling of nuclear waste disposal is modeled as a nonsmooth multiobjective mixed-integer nonlinear optimization problem, and a novel method using the two-slope parameterized achievement scalarizing functions is introduced. Finally, the last paper considers binary classification of a multiple instance learning problem and formulates the learning problem as a nonconvex nonsmooth unconstrained optimization problem with a DC objective function.
Book Synopsis Cluster Analysis and Applications by : Rudolf Scitovski
Download or read book Cluster Analysis and Applications written by Rudolf Scitovski and published by Springer Nature. This book was released on 2021-07-22 with total page 277 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the development of Big Data platforms for managing massive amount of data and wide availability of tools for processing these data, the biggest limitation is the lack of trained experts who are qualified to process and interpret the results. This textbook is intended for graduate students and experts using methods of cluster analysis and applications in various fields. Suitable for an introductory course on cluster analysis or data mining, with an in-depth mathematical treatment that includes discussions on different measures, primitives (points, lines, etc.) and optimization-based clustering methods, Cluster Analysis and Applications also includes coverage of deep learning based clustering methods. With clear explanations of ideas and precise definitions of concepts, accompanied by numerous examples and exercises together with Mathematica programs and modules, Cluster Analysis and Applications may be used by students and researchers in various disciplines, working in data analysis or data science.
Book Synopsis Nonsmooth Optimization Models and Algorithms for Data Clustering and Visualization by : Ehsan Mohebi
Download or read book Nonsmooth Optimization Models and Algorithms for Data Clustering and Visualization written by Ehsan Mohebi and published by . This book was released on 2014 with total page 326 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Cluster analysis deals with the problem of organization of a collection of patterns into clusters based on a similarity measure. Various distance functions can be used to define this measure. Clustering problems with the similarity measure defined by the squared Euclidean distance have been studied extensively over the last five decades. However, problems with other Minkowski norms have attracted significantly less attention. The use of different similarity measures may help to identify different cluster structures of a data set. This in turn may help to significantly improve the decision making process. High dimensional data visualization is another important task in the field of data mining and pattern recognition. To date, the principal component analysis and the self-organizing maps techniques have been used to solve such problems. In this thesis we develop algorithms for solving clustering problems in large data sets using various similarity measures. Such similarity measures are based on the squared L2 as well as L1 and L {infinity symbol} norms. In all cases the clustering problem is a global optimization problem with nonsmooth nonconvex objective functions. In many datasets these problems are large scale and the conventional global optimization algorithms are not efficient for solving such problems. Therefore we propose to apply local search methods for solving clustering problems, however the success of these methods strongly depends on the choice of starting cluster centers. To deal with the nonconvexity of the clustering problems we propose incremental algorithms for their solution which helps us to design a special procedure to generate starting points for cluster centers. Such an approach allows one to find global or near global solutions to the clustering problem. In order to solve nonsmooth clustering problems we apply both efficient nonsmooth optimization algorithms as well as smoothing techniques. To test the proposed algorithms we apply them to solve clustering problems in small, medium size and large data sets. Furthermore, these algorithms are compared with many other clustering algorithms using results of numerical experiments. The Self Organizing Maps (SOM) is one of the topology visualizing tool that contains a set of neurons that gradually adapt to input data space by competitive learning and form clusters. The topology preservation of the SOM strongly depends on the learning process. Due to this limitation one cannot guarantee the convergence of the SOM in data sets with clusters of arbitrary shape. Therefore it is important to develop more accurate data visualization and clustering algorithms. In this thesis, Constrained SOM (CSOM) is proposed as the new version of the SOM by modifying the learning algorithm. The idea is to introduce an adaptive constraint parameter to the learning process to improve the topology preservation and mapping quality of the basic SOM. The computational complexity of the CSOM is less than that of the SOM. Mapping quality of the SOM is sensitive to the map topology and initialization of neurons. Thus in this research, a modified version of the SOM (MSOM) is proposed to improve the convergence of the SOM. An initialization algorithm based on split and merge of clusters is introduced to initialize neurons of the SOM. The initialization algorithm speeds up the learning process in large high dimensional data sets. A topology based on this initialization is developed to minimize the vector quantization error and topology preservation of the self organizing maps. The CSOM and MSOM algorithms are tested on small to large size real-world datasets. Finally, a convolutional structure of the Recursive Modified SOM is proposed to cope with the diversity of styles and shapes in digits recognition. The proposed recursive structure can learn various behaviors of incoming images. The numerical results on the well-known MNIST dataset demonstrate the superiority of the proposed algorithm over existing SOM-based approaches." -- Abstract.
Book Synopsis Artificial Intelligence: Theories and Applications by : Mohammed Salem
Download or read book Artificial Intelligence: Theories and Applications written by Mohammed Salem and published by Springer Nature. This book was released on 2023-03-17 with total page 313 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume constitutes selected papers presented at the First International Conference on Artificial Intelligence: Theories and Applications, ICAITA 2022, held in Mascara, Algeria, in November 2022. The 23 papers were thoroughly reviewed and selected from the 66 qualified submissions. They are organized in topical sections on artificial vision; and articial intelligence in big data and natural language processing.
Book Synopsis Data Classification and Incremental Clustering in Data Mining and Machine Learning by : Sanjay Chakraborty
Download or read book Data Classification and Incremental Clustering in Data Mining and Machine Learning written by Sanjay Chakraborty and published by Springer Nature. This book was released on 2022-05-10 with total page 210 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a comprehensive, hands-on guide to the basics of data mining and machine learning with a special emphasis on supervised and unsupervised learning methods. The book lays stress on the new ways of thinking needed to master in machine learning based on the Python, R, and Java programming platforms. This book first provides an understanding of data mining, machine learning and their applications, giving special attention to classification and clustering techniques. The authors offer a discussion on data mining and machine learning techniques with case studies and examples. The book also describes the hands-on coding examples of some well-known supervised and unsupervised learning techniques using three different and popular coding platforms: R, Python, and Java. This book explains some of the most popular classification techniques (K-NN, Naïve Bayes, Decision tree, Random forest, Support vector machine etc,) along with the basic description of artificial neural network and deep neural network. The book is useful for professionals, students studying data mining and machine learning, and researchers in supervised and unsupervised learning techniques.
Book Synopsis Introduction to Nonsmooth Optimization by : Adil Bagirov
Download or read book Introduction to Nonsmooth Optimization written by Adil Bagirov and published by Springer. This book was released on 2014-08-12 with total page 377 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is the first easy-to-read text on nonsmooth optimization (NSO, not necessarily differentiable optimization). Solving these kinds of problems plays a critical role in many industrial applications and real-world modeling systems, for example in the context of image denoising, optimal control, neural network training, data mining, economics and computational chemistry and physics. The book covers both the theory and the numerical methods used in NSO and provide an overview of different problems arising in the field. It is organized into three parts: 1. convex and nonconvex analysis and the theory of NSO; 2. test problems and practical applications; 3. a guide to NSO software. The book is ideal for anyone teaching or attending NSO courses. As an accessible introduction to the field, it is also well suited as an independent learning guide for practitioners already familiar with the basics of optimization.
Book Synopsis Nonsmooth Optimization: Analysis And Algorithms With Applications To Optimal Control by : Marko M Makela
Download or read book Nonsmooth Optimization: Analysis And Algorithms With Applications To Optimal Control written by Marko M Makela and published by World Scientific. This book was released on 1992-05-07 with total page 268 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a self-contained elementary study for nonsmooth analysis and optimization, and their use in solution of nonsmooth optimal control problems. The first part of the book is concerned with nonsmooth differential calculus containing necessary tools for nonsmooth optimization. The second part is devoted to the methods of nonsmooth optimization and their development. A proximal bundle method for nonsmooth nonconvex optimization subject to nonsmooth constraints is constructed. In the last part nonsmooth optimization is applied to problems arising from optimal control of systems covered by partial differential equations. Several practical problems, like process control and optimal shape design problems are considered.
Book Synopsis Numerical Analysis and Optimization by : Mehiddin Al-Baali
Download or read book Numerical Analysis and Optimization written by Mehiddin Al-Baali and published by Springer. This book was released on 2018-05-31 with total page 311 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume contains 13 selected keynote papers presented at the Fourth International Conference on Numerical Analysis and Optimization. Held every three years at Sultan Qaboos University in Muscat, Oman, this conference highlights novel and advanced applications of recent research in numerical analysis and optimization. Each peer-reviewed chapter featured in this book reports on developments in key fields, such as numerical analysis, numerical optimization, numerical linear algebra, numerical differential equations, optimal control, approximation theory, applied mathematics, derivative-free optimization methods, programming models, and challenging applications that frequently arise in statistics, econometrics, finance, physics, medicine, biology, engineering and industry. Any graduate student or researched wishing to know the latest research in the field will be interested in this volume. This book is dedicated to the late Professors Mike JD Powell and Roger Fletcher, who were the pioneers and leading figures in the mathematics of nonlinear optimization.
Author :Christodoulos A. Floudas Publisher :Springer Science & Business Media ISBN 13 :0387747583 Total Pages :4646 pages Book Rating :4.3/5 (877 download)
Book Synopsis Encyclopedia of Optimization by : Christodoulos A. Floudas
Download or read book Encyclopedia of Optimization written by Christodoulos A. Floudas and published by Springer Science & Business Media. This book was released on 2008-09-04 with total page 4646 pages. Available in PDF, EPUB and Kindle. Book excerpt: The goal of the Encyclopedia of Optimization is to introduce the reader to a complete set of topics that show the spectrum of research, the richness of ideas, and the breadth of applications that has come from this field. The second edition builds on the success of the former edition with more than 150 completely new entries, designed to ensure that the reference addresses recent areas where optimization theories and techniques have advanced. Particularly heavy attention resulted in health science and transportation, with entries such as "Algorithms for Genomics", "Optimization and Radiotherapy Treatment Design", and "Crew Scheduling".
Book Synopsis Introduction to Applied Linear Algebra by : Stephen Boyd
Download or read book Introduction to Applied Linear Algebra written by Stephen Boyd and published by Cambridge University Press. This book was released on 2018-06-07 with total page 477 pages. Available in PDF, EPUB and Kindle. Book excerpt: A groundbreaking introduction to vectors, matrices, and least squares for engineering applications, offering a wealth of practical examples.
Book Synopsis Modern Statistical Methods for Health Research by : Yichuan Zhao
Download or read book Modern Statistical Methods for Health Research written by Yichuan Zhao and published by Springer Nature. This book was released on 2021-10-14 with total page 506 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book brings together the voices of leading experts in the frontiers of biostatistics, biomedicine, and the health sciences to discuss the statistical procedures, useful methods, and novel applications in biostatistics research. It also includes discussions of potential future directions of biomedicine and new statistical developments for health research, with the intent of stimulating research and fostering the interactions of scholars across health research related disciplines. Topics covered include: Health data analysis and applications to EHR data Clinical trials, FDR, and applications in health science Big network analytics and its applications in GWAS Survival analysis and functional data analysis Graphical modelling in genomic studies The book will be valuable to data scientists and statisticians who are working in biomedicine and health, other practitioners in the health sciences, and graduate students and researchers in biostatistics and health.
Book Synopsis Derivative-Free and Blackbox Optimization by : Charles Audet
Download or read book Derivative-Free and Blackbox Optimization written by Charles Audet and published by Springer. This book was released on 2017-12-02 with total page 307 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is designed as a textbook, suitable for self-learning or for teaching an upper-year university course on derivative-free and blackbox optimization. The book is split into 5 parts and is designed to be modular; any individual part depends only on the material in Part I. Part I of the book discusses what is meant by Derivative-Free and Blackbox Optimization, provides background material, and early basics while Part II focuses on heuristic methods (Genetic Algorithms and Nelder-Mead). Part III presents direct search methods (Generalized Pattern Search and Mesh Adaptive Direct Search) and Part IV focuses on model-based methods (Simplex Gradient and Trust Region). Part V discusses dealing with constraints, using surrogates, and bi-objective optimization. End of chapter exercises are included throughout as well as 15 end of chapter projects and over 40 figures. Benchmarking techniques are also presented in the appendix.
Author :Prateek Jain Publisher :Foundations and Trends in Machine Learning ISBN 13 :9781680833683 Total Pages :218 pages Book Rating :4.8/5 (336 download)
Book Synopsis Non-convex Optimization for Machine Learning by : Prateek Jain
Download or read book Non-convex Optimization for Machine Learning written by Prateek Jain and published by Foundations and Trends in Machine Learning. This book was released on 2017-12-04 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt: Non-convex Optimization for Machine Learning takes an in-depth look at the basics of non-convex optimization with applications to machine learning. It introduces the rich literature in this area, as well as equips the reader with the tools and techniques needed to apply and analyze simple but powerful procedures for non-convex problems. Non-convex Optimization for Machine Learning is as self-contained as possible while not losing focus of the main topic of non-convex optimization techniques. The monograph initiates the discussion with entire chapters devoted to presenting a tutorial-like treatment of basic concepts in convex analysis and optimization, as well as their non-convex counterparts. The monograph concludes with a look at four interesting applications in the areas of machine learning and signal processing, and exploring how the non-convex optimization techniques introduced earlier can be used to solve these problems. The monograph also contains, for each of the topics discussed, exercises and figures designed to engage the reader, as well as extensive bibliographic notes pointing towards classical works and recent advances. Non-convex Optimization for Machine Learning can be used for a semester-length course on the basics of non-convex optimization with applications to machine learning. On the other hand, it is also possible to cherry pick individual portions, such the chapter on sparse recovery, or the EM algorithm, for inclusion in a broader course. Several courses such as those in machine learning, optimization, and signal processing may benefit from the inclusion of such topics.
Book Synopsis The ... IEEE International Conference on Fuzzy Systems Proceedings by :
Download or read book The ... IEEE International Conference on Fuzzy Systems Proceedings written by and published by . This book was released on 1998 with total page 586 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Operations Research Proceedings 2008 by : Bernhard Fleischmann
Download or read book Operations Research Proceedings 2008 written by Bernhard Fleischmann and published by Springer Science & Business Media. This book was released on 2009-08-04 with total page 586 pages. Available in PDF, EPUB and Kindle. Book excerpt: The international conference \Operations Research 2008", the annual meeting of the German Operations Research Society (GOR), was held at the University of Augsburg on September 3-5, 2008. About 580 p- ticipants from more than 30 countries presented and listened to nearly 400 talks on a broad range of Operations Research. The general subject \Operations Research and Global Business" str- ses the important role of Operations Research in improving decisions in the increasingly complex business processes in a global environment. The plenary speakers Morris A. Cohen (Wharton School) and Bernd Liepert (Executive Board of KUKA Robotics) addressed this subject. Moreover, one of the founders of Operations Research, Saul Gass (U- versity of Maryland), gave the opening speech on the early history of Operations Research. This volume contains 93 papers presented at the conference, selected by the program committee and the section chairs, forming a representative sample of the various subjects dealt with at Operations Research 2008. The volume follows the structure of the conference, with 12 sections, grouped into six \Fields of Applications" and six \Fields of Methods and Theory". This structure in no way means a separation of theory and application, which would be detrimental in Operations Research, but displays the large spectrum of aspects in the focus of the papers. Of course, most papers present theory, methods and applications together.
