Linear Algebra Tools For Data Mining Second Edition
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Linear Algebra Tools For Data Mining
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Author : Dan A. Simovici
language : en
Publisher: World Scientific
Release Date : 2012
Linear Algebra Tools For Data Mining written by Dan A. Simovici and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with Computers categories.
This comprehensive volume presents the foundations of linear algebra ideas and techniques applied to data mining and related fields. Linear algebra has gained increasing importance in data mining and pattern recognition, as shown by the many current data mining publications, and has a strong impact in other disciplines like psychology, chemistry, and biology. The basic material is accompanied by more than 550 exercises and supplements, many accompanied with complete solutions and MATLAB applications.
Linear Algebra Tools For Data Mining Second Edition
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Author : Dan A Simovici
language : en
Publisher: World Scientific
Release Date : 2023-06-16
Linear Algebra Tools For Data Mining Second Edition written by Dan A Simovici and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-16 with Computers categories.
This updated compendium provides the linear algebra background necessary to understand and develop linear algebra applications in data mining and machine learning.Basic knowledge and advanced new topics (spectral theory, singular values, decomposition techniques for matrices, tensors and multidimensional arrays) are presented together with several applications of linear algebra (k-means clustering, biplots, least square approximations, dimensionality reduction techniques, tensors and multidimensional arrays).The useful reference text includes more than 600 exercises and supplements, many with completed solutions and MATLAB applications.The volume benefits professionals, academics, researchers and graduate students in the fields of pattern recognition/image analysis, AI, machine learning and databases.
Matrix Methods In Data Mining And Pattern Recognition
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Author : Lars Elden
language : en
Publisher: SIAM
Release Date : 2007-07-12
Matrix Methods In Data Mining And Pattern Recognition written by Lars Elden and has been published by SIAM this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-07-12 with Computers categories.
Several very powerful numerical linear algebra techniques are available for solving problems in data mining and pattern recognition. This application-oriented book describes how modern matrix methods can be used to solve these problems, gives an introduction to matrix theory and decompositions, and provides students with a set of tools that can be modified for a particular application.Matrix Methods in Data Mining and Pattern Recognition is divided into three parts. Part I gives a short introduction to a few application areas before presenting linear algebra concepts and matrix decompositions that students can use in problem-solving environments such as MATLAB®. Some mathematical proofs that emphasize the existence and properties of the matrix decompositions are included. In Part II, linear algebra techniques are applied to data mining problems. Part III is a brief introduction to eigenvalue and singular value algorithms. The applications discussed by the author are: classification of handwritten digits, text mining, text summarization, pagerank computations related to the GoogleÔ search engine, and face recognition. Exercises and computer assignments are available on a Web page that supplements the book.Audience The book is intended for undergraduate students who have previously taken an introductory scientific computing/numerical analysis course. Graduate students in various data mining and pattern recognition areas who need an introduction to linear algebra techniques will also find the book useful.Contents Preface; Part I: Linear Algebra Concepts and Matrix Decompositions. Chapter 1: Vectors and Matrices in Data Mining and Pattern Recognition; Chapter 2: Vectors and Matrices; Chapter 3: Linear Systems and Least Squares; Chapter 4: Orthogonality; Chapter 5: QR Decomposition; Chapter 6: Singular Value Decomposition; Chapter 7: Reduced-Rank Least Squares Models; Chapter 8: Tensor Decomposition; Chapter 9: Clustering and Nonnegative Matrix Factorization; Part II: Data Mining Applications. Chapter 10: Classification of Handwritten Digits; Chapter 11: Text Mining; Chapter 12: Page Ranking for a Web Search Engine; Chapter 13: Automatic Key Word and Key Sentence Extraction; Chapter 14: Face Recognition Using Tensor SVD. Part III: Computing the Matrix Decompositions. Chapter 15: Computing Eigenvalues and Singular Values; Bibliography; Index.
Linear Algebra Tools For Data Mining
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Author : Dan A Simovici
language : en
Publisher: World Scientific
Release Date : 2012-01-27
Linear Algebra Tools For Data Mining written by Dan A Simovici and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-01-27 with Computers categories.
This comprehensive volume presents the foundations of linear algebra ideas and techniques applied to data mining and related fields. Linear algebra has gained increasing importance in data mining and pattern recognition, as shown by the many current data mining publications, and has a strong impact in other disciplines like psychology, chemistry, and biology. The basic material is accompanied by more than 550 exercises and supplements, many accompanied with complete solutions and MATLAB applications.
Data Mining
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Author : Ian H. Witten
language : en
Publisher: Elsevier
Release Date : 2005-07-13
Data Mining written by Ian H. Witten and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-07-13 with Computers categories.
Data Mining, Second Edition, describes data mining techniques and shows how they work. The book is a major revision of the first edition that appeared in 1999. While the basic core remains the same, it has been updated to reflect the changes that have taken place over five years, and now has nearly double the references. The highlights of this new edition include thirty new technique sections; an enhanced Weka machine learning workbench, which now features an interactive interface; comprehensive information on neural networks; a new section on Bayesian networks; and much more. This text is designed for information systems practitioners, programmers, consultants, developers, information technology managers, specification writers as well as professors and students of graduate-level data mining and machine learning courses. - Algorithmic methods at the heart of successful data mining—including tried and true techniques as well as leading edge methods - Performance improvement techniques that work by transforming the input or output
Matrix Analysis And Applied Linear Algebra
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Author : Carl D. Meyer
language : en
Publisher: SIAM
Release Date : 2000-01-01
Matrix Analysis And Applied Linear Algebra written by Carl D. Meyer and has been published by SIAM this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000-01-01 with Mathematics categories.
Matrix Analysis and Applied Linear Algebra is an honest math text that circumvents the traditional definition-theorem-proof format that has bored students in the past. Meyer uses a fresh approach to introduce a variety of problems and examples ranging from the elementary to the challenging and from simple applications to discovery problems. The focus on applications is a big difference between this book and others. Meyer's book is more rigorous and goes into more depth than some. He includes some of the more contemporary topics of applied linear algebra which are not normally found in undergraduate textbooks. Modern concepts and notation are used to introduce the various aspects of linear equations, leading readers easily to numerical computations and applications. The theoretical developments are always accompanied with examples, which are worked out in detail. Each section ends with a large number of carefully chosen exercises from which the students can gain further insight.
Understanding Complex Datasets
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Author : David Skillicorn
language : en
Publisher: CRC Press
Release Date : 2007-05-17
Understanding Complex Datasets written by David Skillicorn and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-05-17 with Computers categories.
Making obscure knowledge about matrix decompositions widely available, Understanding Complex Datasets: Data Mining with Matrix Decompositions discusses the most common matrix decompositions and shows how they can be used to analyze large datasets in a broad range of application areas. Without having to understand every mathematical detail, the book
Mathematical Tools For Data Mining
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Author : Dan A. Simovici
language : en
Publisher: Springer Science & Business Media
Release Date : 2014-03-27
Mathematical Tools For Data Mining written by Dan A. Simovici 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 2014-03-27 with Computers categories.
Data mining essentially relies on several mathematical disciplines, many of which are presented in this second edition of this book. Topics include partially ordered sets, combinatorics, general topology, metric spaces, linear spaces, graph theory. To motivate the reader a significant number of applications of these mathematical tools are included ranging from association rules, clustering algorithms, classification, data constraints, logical data analysis, etc. The book is intended as a reference for researchers and graduate students. The current edition is a significant expansion of the first edition. We strived to make the book self-contained and only a general knowledge of mathematics is required. More than 700 exercises are included and they form an integral part of the material. Many exercises are in reality supplemental material and their solutions are included.
Machine Learning For Data Streams
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Author : Albert Bifet
language : en
Publisher: MIT Press
Release Date : 2018-03-16
Machine Learning For Data Streams written by Albert Bifet and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-03-16 with Computers categories.
A hands-on approach to tasks and techniques in data stream mining and real-time analytics, with examples in MOA, a popular freely available open-source software framework. Today many information sources—including sensor networks, financial markets, social networks, and healthcare monitoring—are so-called data streams, arriving sequentially and at high speed. Analysis must take place in real time, with partial data and without the capacity to store the entire data set. This book presents algorithms and techniques used in data stream mining and real-time analytics. Taking a hands-on approach, the book demonstrates the techniques using MOA (Massive Online Analysis), a popular, freely available open-source software framework, allowing readers to try out the techniques after reading the explanations. The book first offers a brief introduction to the topic, covering big data mining, basic methodologies for mining data streams, and a simple example of MOA. More detailed discussions follow, with chapters on sketching techniques, change, classification, ensemble methods, regression, clustering, and frequent pattern mining. Most of these chapters include exercises, an MOA-based lab session, or both. Finally, the book discusses the MOA software, covering the MOA graphical user interface, the command line, use of its API, and the development of new methods within MOA. The book will be an essential reference for readers who want to use data stream mining as a tool, researchers in innovation or data stream mining, and programmers who want to create new algorithms for MOA.
Sketching As A Tool For Numerical Linear Algebra
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Author : David P. Woodruff
language : en
Publisher: Now Publishers
Release Date : 2014-11-14
Sketching As A Tool For Numerical Linear Algebra written by David P. Woodruff and has been published by Now Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-11-14 with Computers categories.
Sketching as a Tool for Numerical Linear Algebra highlights the recent advances in algorithms for numerical linear algebra that have come from the technique of linear sketching, whereby given a matrix, one first compressed it to a much smaller matrix by multiplying it by a (usually) random matrix with certain properties. Much of the expensive computation can then be performed on the smaller matrix, thereby accelerating the solution for the original problem. It is an ideal primer for researchers and students of theoretical computer science interested in how sketching techniques can be used to speed up numerical linear algebra applications.