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Author: Kimito Funatsu Publisher: BoD – Books on Demand ISBN: 9533075473 Category : Computers Languages : en Pages : 600
Book Description
The progress of data mining technology and large public popularity establish a need for a comprehensive text on the subject. The series of books entitled by "Data Mining" address the need by presenting in-depth description of novel mining algorithms and many useful applications. In addition to understanding each section deeply, the two books present useful hints and strategies to solving problems in the following chapters. The contributing authors have highlighted many future research directions that will foster multi-disciplinary collaborations and hence will lead to significant development in the field of data mining.
Author: Kimito Funatsu Publisher: BoD – Books on Demand ISBN: 9533075473 Category : Computers Languages : en Pages : 600
Book Description
The progress of data mining technology and large public popularity establish a need for a comprehensive text on the subject. The series of books entitled by "Data Mining" address the need by presenting in-depth description of novel mining algorithms and many useful applications. In addition to understanding each section deeply, the two books present useful hints and strategies to solving problems in the following chapters. The contributing authors have highlighted many future research directions that will foster multi-disciplinary collaborations and hence will lead to significant development in the field of data mining.
Author: Werner Dubitzky Publisher: Springer Science & Business Media ISBN: 0387475095 Category : Science Languages : en Pages : 300
Book Description
This book presents state-of-the-art analytical methods from statistics and data mining for the analysis of high-throughput data from genomics and proteomics. It adopts an approach focusing on concepts and applications and presents key analytical techniques for the analysis of genomics and proteomics data by detailing their underlying principles, merits and limitations.
Author: Charu C. Aggarwal Publisher: Springer ISBN: 3319141422 Category : Computers Languages : en Pages : 746
Book Description
This textbook explores the different aspects of data mining from the fundamentals to the complex data types and their applications, capturing the wide diversity of problem domains for data mining issues. It goes beyond the traditional focus on data mining problems to introduce advanced data types such as text, time series, discrete sequences, spatial data, graph data, and social networks. Until now, no single book has addressed all these topics in a comprehensive and integrated way. The chapters of this book fall into one of three categories: Fundamental chapters: Data mining has four main problems, which correspond to clustering, classification, association pattern mining, and outlier analysis. These chapters comprehensively discuss a wide variety of methods for these problems. Domain chapters: These chapters discuss the specific methods used for different domains of data such as text data, time-series data, sequence data, graph data, and spatial data. Application chapters: These chapters study important applications such as stream mining, Web mining, ranking, recommendations, social networks, and privacy preservation. The domain chapters also have an applied flavor. Appropriate for both introductory and advanced data mining courses, Data Mining: The Textbook balances mathematical details and intuition. It contains the necessary mathematical details for professors and researchers, but it is presented in a simple and intuitive style to improve accessibility for students and industrial practitioners (including those with a limited mathematical background). Numerous illustrations, examples, and exercises are included, with an emphasis on semantically interpretable examples. Praise for Data Mining: The Textbook - “As I read through this book, I have already decided to use it in my classes. This is a book written by an outstanding researcher who has made fundamental contributions to data mining, in a way that is both accessible and up to date. The book is complete with theory and practical use cases. It’s a must-have for students and professors alike!" -- Qiang Yang, Chair of Computer Science and Engineering at Hong Kong University of Science and Technology "This is the most amazing and comprehensive text book on data mining. It covers not only the fundamental problems, such as clustering, classification, outliers and frequent patterns, and different data types, including text, time series, sequences, spatial data and graphs, but also various applications, such as recommenders, Web, social network and privacy. It is a great book for graduate students and researchers as well as practitioners." -- Philip S. Yu, UIC Distinguished Professor and Wexler Chair in Information Technology at University of Illinois at Chicago
Author: Ian H. Witten Publisher: Elsevier ISBN: 0080890369 Category : Computers Languages : en Pages : 665
Book Description
Data Mining: Practical Machine Learning Tools and Techniques, Third Edition, offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. The book is targeted at information systems practitioners, programmers, consultants, developers, information technology managers, specification writers, data analysts, data modelers, database R&D professionals, data warehouse engineers, data mining professionals. The book will also be useful for professors and students of upper-level undergraduate and graduate-level data mining and machine learning courses who want to incorporate data mining as part of their data management knowledge base and expertise. - Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects - Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods - Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks—in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization
Author: Taniar, David Publisher: IGI Global ISBN: 1613504756 Category : Computers Languages : en Pages : 465
Book Description
"This book is an updated look at the state of technology in the field of data mining and analytics offering the latest technological, analytical, ethical, and commercial perspectives on topics in data mining"--Provided by publisher.
Author: Bhavani Thuraisingham Publisher: CRC Press ISBN: 1482252503 Category : Business & Economics Languages : en Pages : 292
Book Description
Focusing on a data-centric perspective, this book provides a complete overview of data mining: its uses, methods, current technologies, commercial products, and future challenges. Three parts divide Data Mining: Part I describes technologies for data mining - database systems, warehousing, machine learning, visualization, decision sup
Author: Mohammed J. Zaki Publisher: Cambridge University Press ISBN: 1108658695 Category : Computers Languages : en Pages : 780
Book Description
The fundamental algorithms in data mining and machine learning form the basis of data science, utilizing automated methods to analyze patterns and models for all kinds of data in applications ranging from scientific discovery to business analytics. This textbook for senior undergraduate and graduate courses provides a comprehensive, in-depth overview of data mining, machine learning and statistics, offering solid guidance for students, researchers, and practitioners. The book lays the foundations of data analysis, pattern mining, clustering, classification and regression, with a focus on the algorithms and the underlying algebraic, geometric, and probabilistic concepts. New to this second edition is an entire part devoted to regression methods, including neural networks and deep learning.
Author: Xin-She Yang Publisher: Academic Press ISBN: 0128172177 Category : Mathematics Languages : en Pages : 188
Book Description
Introduction to Algorithms for Data Mining and Machine Learning introduces the essential ideas behind all key algorithms and techniques for data mining and machine learning, along with optimization techniques. Its strong formal mathematical approach, well selected examples, and practical software recommendations help readers develop confidence in their data modeling skills so they can process and interpret data for classification, clustering, curve-fitting and predictions. Masterfully balancing theory and practice, it is especially useful for those who need relevant, well explained, but not rigorous (proofs based) background theory and clear guidelines for working with big data. Presents an informal, theorem-free approach with concise, compact coverage of all fundamental topics Includes worked examples that help users increase confidence in their understanding of key algorithms, thus encouraging self-study Provides algorithms and techniques that can be implemented in any programming language, with each chapter including notes about relevant software packages
Author: Hillol Kargupta Publisher: Chapman and Hall/CRC ISBN: 9781420085860 Category : Computers Languages : en Pages : 601
Book Description
Drawn from the US National Science Foundation’s Symposium on Next Generation of Data Mining and Cyber-Enabled Discovery for Innovation (NGDM 07), Next Generation of Data Mining explores emerging technologies and applications in data mining as well as potential challenges faced by the field. Gathering perspectives from top experts across different disciplines, the book debates upcoming challenges and outlines computational methods. The contributors look at how ecology, astronomy, social science, medicine, finance, and more can benefit from the next generation of data mining techniques. They examine the algorithms, middleware, infrastructure, and privacy policies associated with ubiquitous, distributed, and high performance data mining. They also discuss the impact of new technologies, such as the semantic web, on data mining and provide recommendations for privacy-preserving mechanisms. The dramatic increase in the availability of massive, complex data from various sources is creating computing, storage, communication, and human-computer interaction challenges for data mining. Providing a framework to better understand these fundamental issues, this volume surveys promising approaches to data mining problems that span an array of disciplines.
Author: Mohammed J. Zaki Publisher: Cambridge University Press ISBN: 0521766338 Category : Computers Languages : en Pages : 607
Book Description
A comprehensive overview of data mining from an algorithmic perspective, integrating related concepts from machine learning and statistics.
Author: Kimito Funatsu
Author: Kimito Funatsu
Author: Werner Dubitzky
Author: Charu C. Aggarwal
Author: Ian H. Witten
Author: Taniar, David
Author: Bhavani Thuraisingham
Author: Mohammed J. Zaki
Author: Xin-She Yang
Author: Hillol Kargupta
Author: Mohammed J. Zaki