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Author: M. Emre Celebi
Publisher: Springer
ISBN: 3319092596
Category : Technology & Engineering
Languages : en
Pages : 415
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Book Description
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.
Author: M. Emre Celebi
Publisher: Springer
ISBN: 3319092596
Category : Technology & Engineering
Languages : en
Pages : 415
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Book Description
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.
Author: Adil M. Bagirov
Publisher: Springer Nature
ISBN: 3030378268
Category : Technology & Engineering
Languages : en
Pages : 343
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Book Description
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.
Author: Harun Pirim
Publisher: BoD – Books on Demand
ISBN: 178923526X
Category : Computers
Languages : en
Pages : 250
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Book Description
Clustering has emerged as one of the more fertile fields within data analytics, widely adopted by companies, research institutions, and educational entities as a tool to describe similar/different groups. The book Recent Applications in Data Clustering aims to provide an outlook of recent contributions to the vast clustering literature that offers useful insights within the context of modern applications for professionals, academics, and students. The book spans the domains of clustering in image analysis, lexical analysis of texts, replacement of missing values in data, temporal clustering in smart cities, comparison of artificial neural network variations, graph theoretical approaches, spectral clustering, multiview clustering, and model-based clustering in an R package. Applications of image, text, face recognition, speech (synthetic and simulated), and smart city datasets are presented.
Author: Guojun Gan
Publisher: SIAM
ISBN: 1611976332
Category : Mathematics
Languages : en
Pages : 430
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Book Description
Data clustering, also known as cluster analysis, is an unsupervised process that divides a set of objects into homogeneous groups. Since the publication of the first edition of this monograph in 2007, development in the area has exploded, especially in clustering algorithms for big data and open-source software for cluster analysis. This second edition reflects these new developments, covers the basics of data clustering, includes a list of popular clustering algorithms, and provides program code that helps users implement clustering algorithms. Data Clustering: Theory, Algorithms and Applications, Second Edition will be of interest to researchers, practitioners, and data scientists as well as undergraduate and graduate students.
Author: Charu C. Aggarwal
Publisher: CRC Press
ISBN: 1315362783
Category : Business & Economics
Languages : en
Pages : 652
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Book Description
Research on the problem of clustering tends to be fragmented across the pattern recognition, database, data mining, and machine learning communities. Addressing this problem in a unified way, Data Clustering: Algorithms and Applications provides complete coverage of the entire area of clustering, from basic methods to more refined and complex data clustering approaches. It pays special attention to recent issues in graphs, social networks, and other domains. The book focuses on three primary aspects of data clustering: Methods, describing key techniques commonly used for clustering, such as feature selection, agglomerative clustering, partitional clustering, density-based clustering, probabilistic clustering, grid-based clustering, spectral clustering, and nonnegative matrix factorization Domains, covering methods used for different domains of data, such as categorical data, text data, multimedia data, graph data, biological data, stream data, uncertain data, time series clustering, high-dimensional clustering, and big data Variations and Insights, discussing important variations of the clustering process, such as semisupervised clustering, interactive clustering, multiview clustering, cluster ensembles, and cluster validation In this book, top researchers from around the world explore the characteristics of clustering problems in a variety of application areas. They also explain how to glean detailed insight from the clustering process—including how to verify the quality of the underlying clusters—through supervision, human intervention, or the automated generation of alternative clusters.
Author: Giuseppe Ciaburro
Publisher: Packt Publishing Ltd
ISBN: 1788399390
Category : Computers
Languages : en
Pages : 374
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Book Description
Extract patterns and knowledge from your data in easy way using MATLAB About This Book Get your first steps into machine learning with the help of this easy-to-follow guide Learn regression, clustering, classification, predictive analytics, artificial neural networks and more with MATLAB Understand how your data works and identify hidden layers in the data with the power of machine learning. Who This Book Is For This book is for data analysts, data scientists, students, or anyone who is looking to get started with machine learning and want to build efficient data processing and predicting applications. A mathematical and statistical background will really help in following this book well. What You Will Learn Learn the introductory concepts of machine learning. Discover different ways to transform data using SAS XPORT, import and export tools, Explore the different types of regression techniques such as simple & multiple linear regression, ordinary least squares estimation, correlations and how to apply them to your data. Discover the basics of classification methods and how to implement Naive Bayes algorithm and Decision Trees in the Matlab environment. Uncover how to use clustering methods like hierarchical clustering to grouping data using the similarity measures. Know how to perform data fitting, pattern recognition, and clustering analysis with the help of MATLAB Neural Network Toolbox. Learn feature selection and extraction for dimensionality reduction leading to improved performance. In Detail MATLAB is the language of choice for many researchers and mathematics experts for machine learning. This book will help you build a foundation in machine learning using MATLAB for beginners. You'll start by getting your system ready with t he MATLAB environment for machine learning and you'll see how to easily interact with the Matlab workspace. We'll then move on to data cleansing, mining and analyzing various data types in machine learning and you'll see how to display data values on a plot. Next, you'll get to know about the different types of regression techniques and how to apply them to your data using the MATLAB functions. You'll understand the basic concepts of neural networks and perform data fitting, pattern recognition, and clustering analysis. Finally, you'll explore feature selection and extraction techniques for dimensionality reduction for performance improvement. At the end of the book, you will learn to put it all together into real-world cases covering major machine learning algorithms and be comfortable in performing machine learning with MATLAB. Style and approach The book takes a very comprehensive approach to enhance your understanding of machine learning using MATLAB. Sufficient real-world examples and use cases are included in the book to help you grasp the concepts quickly and apply them easily in your day-to-day work.
Author: Olfa Nasraoui
Publisher: Springer
ISBN: 3319978640
Category : Technology & Engineering
Languages : en
Pages : 187
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Book Description
This book highlights the state of the art and recent advances in Big Data clustering methods and their innovative applications in contemporary AI-driven systems. The book chapters discuss Deep Learning for Clustering, Blockchain data clustering, Cybersecurity applications such as insider threat detection, scalable distributed clustering methods for massive volumes of data; clustering Big Data Streams such as streams generated by the confluence of Internet of Things, digital and mobile health, human-robot interaction, and social networks; Spark-based Big Data clustering using Particle Swarm Optimization; and Tensor-based clustering for Web graphs, sensor streams, and social networks. The chapters in the book include a balanced coverage of big data clustering theory, methods, tools, frameworks, applications, representation, visualization, and clustering validation.
Author: Chi Hau Chen
Publisher: World Scientific
ISBN: 9814497649
Category : Computers
Languages : en
Pages : 1045
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Book Description
The very significant advances in computer vision and pattern recognition and their applications in the last few years reflect the strong and growing interest in the field as well as the many opportunities and challenges it offers. The second edition of this handbook represents both the latest progress and updated knowledge in this dynamic field. The applications and technological issues are particularly emphasized in this edition to reflect the wide applicability of the field in many practical problems. To keep the book in a single volume, it is not possible to retain all chapters of the first edition. However, the chapters of both editions are well written for permanent reference. This indispensable handbook will continue to serve as an authoritative and comprehensive guide in the field.
Author: Paul, Dimple Valayil
Publisher: IGI Global
ISBN: 1799837734
Category : Computers
Languages : en
Pages : 229
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Book Description
The main problems that prevent fast and high-quality document processing in electronic document management systems are insufficient and unstructured information, information redundancy, and the presence of large amounts of undesirable user information. The human factor has a significant impact on the efficiency of document search. An average user is not aware of the advanced option of a query language and uses typical queries. Development of a specialized software toolkit intended for information systems and electronic document management systems can be an effective solution of the tasks listed above. Such toolkits should be based on the means and methods of automatic keyword extraction and text classification. The categorization (or classification) of texts into predefined categories has witnessed a booming interest in the last 10 years due to the increased availability of documents in digital form and the ensuing need to organize them. Thus, research on keyword extraction, advancements in the field, and possible future solutions is of great importance in current times. Developing a Keyword Extractor and Document Classifier: Emerging Research and Opportunities presents an information extraction mechanism that can process many kinds of inputs, realize the type of text, and understand the percentage of the keywords that has to be stored. This mechanism then supports information extraction and information categorization mechanisms. This module is used to support a text summarization mechanism, which leads—with the help of the keyword extraction module—to text categorization. It employs lexical and information retrieval techniques to extract phrases from the document text that are likely to characterize it and determines the category of the retrieved text to present a summary to the users. This book is ideal for practitioners, stakeholders, researchers, academicians, and students who are interested in the development of a new keyword extractor and document classifier method.
Author: Kiri Lou Wagstaff
Publisher:
ISBN:
Category :
Languages : en
Pages : 384
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Book Description
