Linear Algebra Tools For Data Mining

Linear Algebra Tools for Data Mining PDF
Author: Dan A. Simovici
Publisher: World Scientific
ISBN: 981438349X
Release: 2012
Size: 14.67 MB
Format: PDF, ePub, Mobi
Category : Computers
Languages : en
Pages : 863
View: 4021

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Linear Algebra Tools For Data Mining by Dan A. Simovici, Linear Algebra Tools For Data Mining Books available in PDF, EPUB, Mobi Format. Download Linear Algebra Tools For Data Mining books, 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. Key Features Integrates the mathematical developments to their applications in data mining without sacrificing the mathematical rigor Presented applications with full mathematical justifications and are often accompanied by MATLAB code Highlights strong links between linear algebra, topology and graph theory because these links are essentially important for applications A self-contained book that deals with mathematics that is immediately relevant for data mining Book jacket.



Matrix Methods In Data Mining And Pattern Recognition Second Edition

Matrix Methods in Data Mining and Pattern Recognition  Second Edition PDF
Author: Lars Elden
Publisher: SIAM
ISBN: 1611975867
Release: 2019-08-30
Size: 33.66 MB
Format: PDF, Kindle
Category : Mathematics
Languages : en
Pages : 229
View: 6611

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Matrix Methods In Data Mining And Pattern Recognition Second Edition PDF Full

Linear Algebra Tools For Data Mining by Lars Elden, Matrix Methods In Data Mining And Pattern Recognition Second Edition Books available in PDF, EPUB, Mobi Format. Download Matrix Methods In Data Mining And Pattern Recognition Second Edition books, This thoroughly revised second edition provides an updated treatment of numerical linear algebra techniques for solving problems in data mining and pattern recognition. Adopting an application-oriented approach, the author introduces matrix theory and decompositions, describes how modern matrix methods can be applied in real life scenarios, and provides a set of tools that students can modify for a particular application. Building on material from the first edition, the author discusses basic graph concepts and their matrix counterparts. He introduces the graph Laplacian and properties of its eigenvectors needed in spectral partitioning and describes spectral graph partitioning applied to social networks and text classification. Examples are included to help readers visualize the results. This new edition also presents matrix-based methods that underlie many of the algorithms used for big data. The book provides a solid foundation to further explore related topics and presents applications such as classification of handwritten digits, text mining, text summarization, PageRank computations related to the Google search engine, and facial recognition. Exercises and computer assignments are available on a Web page that supplements the book. This book is primarily for undergraduate students who have previously taken an introductory scientific computing/numerical analysis course and graduate students in data mining and pattern recognition areas who need an introduction to linear algebra techniques.



Mathematical Tools For Data Mining

Mathematical Tools for Data Mining PDF
Author: Dan A. Simovici
Publisher: Springer Science & Business Media
ISBN: 1848002017
Release: 2008-08-15
Size: 14.76 MB
Format: PDF, ePub, Docs
Category : Computers
Languages : en
Pages : 615
View: 7565

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Linear Algebra Tools For Data Mining by Dan A. Simovici, Mathematical Tools For Data Mining Books available in PDF, EPUB, Mobi Format. Download Mathematical Tools For Data Mining books, This volume was born from the experience of the authors as researchers and educators,whichsuggeststhatmanystudentsofdataminingarehandicapped in their research by the lack of a formal, systematic education in its mat- matics. The data mining literature contains many excellent titles that address the needs of users with a variety of interests ranging from decision making to p- tern investigation in biological data. However, these books do not deal with the mathematical tools that are currently needed by data mining researchers and doctoral students. We felt it timely to produce a book that integrates the mathematics of data mining with its applications. We emphasize that this book is about mathematical tools for data mining and not about data mining itself; despite this, a substantial amount of applications of mathematical c- cepts in data mining are presented. The book is intended as a reference for the working data miner. In our opinion, three areas of mathematics are vital for data mining: set theory,includingpartially orderedsetsandcombinatorics;linear algebra,with its many applications in principal component analysis and neural networks; and probability theory, which plays a foundational role in statistics, machine learning and data mining. Thisvolumeisdedicatedtothestudyofset-theoreticalfoundationsofdata mining. Two further volumes are contemplated that will cover linear algebra and probability theory. The ?rst part of this book, dedicated to set theory, begins with a study of functionsandrelations.Applicationsofthesefundamentalconceptstosuch- sues as equivalences and partitions are discussed. Also, we prepare the ground for the following volumes by discussing indicator functions, ?elds and?-?elds, and other concepts.



Understanding Complex Datasets

Understanding Complex Datasets PDF
Author: David Skillicorn
Publisher: CRC Press
ISBN: 9781584888338
Release: 2007-05-17
Size: 33.74 MB
Format: PDF, ePub
Category : Computers
Languages : en
Pages : 260
View: 2351

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Linear Algebra Tools For Data Mining by David Skillicorn, Understanding Complex Datasets Books available in PDF, EPUB, Mobi Format. Download Understanding Complex Datasets books, 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 helps you determine which matrix is appropriate for your dataset and what the results mean. Explaining the effectiveness of matrices as data analysis tools, the book illustrates the ability of matrix decompositions to provide more powerful analyses and to produce cleaner data than more mainstream techniques. The author explores the deep connections between matrix decompositions and structures within graphs, relating the PageRank algorithm of Google's search engine to singular value decomposition. He also covers dimensionality reduction, collaborative filtering, clustering, and spectral analysis. With numerous figures and examples, the book shows how matrix decompositions can be used to find documents on the Internet, look for deeply buried mineral deposits without drilling, explore the structure of proteins, detect suspicious emails or cell phone calls, and more. Concentrating on data mining mechanics and applications, this resource helps you model large, complex datasets and investigate connections between standard data mining techniques and matrix decompositions.



Linear Algebra For The 21st Century

Linear Algebra for the 21st Century PDF
Author: Anthony Roberts
Publisher: Oxford University Press, USA
ISBN: 0198856393
Release: 2020-06-29
Size: 46.60 MB
Format: PDF, Docs
Category : Mathematics
Languages : en
Pages : 688
View: 3641

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Linear Algebra For The 21st Century PDF Full

Linear Algebra Tools For Data Mining by Anthony Roberts, Linear Algebra For The 21st Century Books available in PDF, EPUB, Mobi Format. Download Linear Algebra For The 21st Century books, Linear Algebra for 21st Century Applications adapts linear algebra to best suit modern teaching and application, and it places SVD as central to the text early on to empower the students in these disciplines to learn and use the best techniques.



Linear Algebra And Matrix Analysis For Statistics

Linear Algebra and Matrix Analysis for Statistics PDF
Author: Sudipto Banerjee
Publisher: CRC Press
ISBN: 1420095382
Release: 2014-06-06
Size: 47.65 MB
Format: PDF, ePub, Mobi
Category : Mathematics
Languages : en
Pages : 580
View: 352

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Linear Algebra And Matrix Analysis For Statistics PDF Full

Linear Algebra Tools For Data Mining by Sudipto Banerjee, Linear Algebra And Matrix Analysis For Statistics Books available in PDF, EPUB, Mobi Format. Download Linear Algebra And Matrix Analysis For Statistics books, Linear Algebra and Matrix Analysis for Statistics offers a gradual exposition to linear algebra without sacrificing the rigor of the subject. It presents both the vector space approach and the canonical forms in matrix theory. The book is as self-contained as possible, assuming no prior knowledge of linear algebra. The authors first address the rudimentary mechanics of linear systems using Gaussian elimination and the resulting decompositions. They introduce Euclidean vector spaces using less abstract concepts and make connections to systems of linear equations wherever possible. After illustrating the importance of the rank of a matrix, they discuss complementary subspaces, oblique projectors, orthogonality, orthogonal projections and projectors, and orthogonal reduction. The text then shows how the theoretical concepts developed are handy in analyzing solutions for linear systems. The authors also explain how determinants are useful for characterizing and deriving properties concerning matrices and linear systems. They then cover eigenvalues, eigenvectors, singular value decomposition, Jordan decomposition (including a proof), quadratic forms, and Kronecker and Hadamard products. The book concludes with accessible treatments of advanced topics, such as linear iterative systems, convergence of matrices, more general vector spaces, linear transformations, and Hilbert spaces.



Mathematics For Machine Learning

Mathematics for Machine Learning PDF
Author: Marc Peter Deisenroth
Publisher: Cambridge University Press
ISBN: 1108569323
Release: 2020-04-23
Size: 18.66 MB
Format: PDF, Kindle
Category : Computers
Languages : en
Pages :
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Mathematics For Machine Learning PDF Full

Linear Algebra Tools For Data Mining by Marc Peter Deisenroth, Mathematics For Machine Learning Books available in PDF, EPUB, Mobi Format. Download Mathematics For Machine Learning books, The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.



Matrix Methods In Data Mining And Pattern Recognition

Matrix Methods in Data Mining and Pattern Recognition PDF
Author: Lars Eldén
Publisher: Society for Industrial and Applied Mathematics
ISBN: 9780898716269
Release: 2007-07-12
Size: 78.24 MB
Format: PDF, Kindle
Category : Computers
Languages : en
Pages : 184
View: 1860

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Matrix Methods In Data Mining And Pattern Recognition PDF Full

Linear Algebra Tools For Data Mining by Lars Eldén, Matrix Methods In Data Mining And Pattern Recognition Books available in PDF, EPUB, Mobi Format. Download Matrix Methods In Data Mining And Pattern Recognition books, 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. 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. 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 include classification of handwritten digits, text mining, text summarization, pagerank computations related to the Google search engine, and face recognition. Exercises and computer assignments are available on a Web page that supplements the book.



Basics Of Linear Algebra For Machine Learning

Basics of Linear Algebra for Machine Learning PDF
Author: Jason Brownlee
Publisher: Machine Learning Mastery
ISBN:
Release: 2018-01-24
Size: 73.88 MB
Format: PDF, ePub
Category : Computers
Languages : en
Pages : 211
View: 3167

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Basics Of Linear Algebra For Machine Learning PDF Full

Linear Algebra Tools For Data Mining by Jason Brownlee, Basics Of Linear Algebra For Machine Learning Books available in PDF, EPUB, Mobi Format. Download Basics Of Linear Algebra For Machine Learning books, Linear algebra is a pillar of machine learning. You cannot develop a deep understanding and application of machine learning without it. In this laser-focused Ebook, you will finally cut through the equations, Greek letters, and confusion, and discover the topics in linear algebra that you need to know. Using clear explanations, standard Python libraries, and step-by-step tutorial lessons, you will discover what linear algebra is, the importance of linear algebra to machine learning, vector, and matrix operations, matrix factorization, principal component analysis, and much more.



Linear Algebra And Learning From Data

Linear Algebra and Learning from Data PDF
Author: Gilbert Strang
Publisher: Wellesley-Cambridge Press
ISBN: 9780692196380
Release: 2019-01-31
Size: 70.42 MB
Format: PDF, ePub
Category : Computers
Languages : en
Pages : 446
View: 5651

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Linear Algebra And Learning From Data PDF Full

Linear Algebra Tools For Data Mining by Gilbert Strang, Linear Algebra And Learning From Data Books available in PDF, EPUB, Mobi Format. Download Linear Algebra And Learning From Data books, Linear algebra and the foundations of deep learning, together at last! From Professor Gilbert Strang, acclaimed author of Introduction to Linear Algebra, comes Linear Algebra and Learning from Data, the first textbook that teaches linear algebra together with deep learning and neural nets. This readable yet rigorous textbook contains a complete course in the linear algebra and related mathematics that students need to know to get to grips with learning from data. Included are: the four fundamental subspaces, singular value decompositions, special matrices, large matrix computation techniques, compressed sensing, probability and statistics, optimization, the architecture of neural nets, stochastic gradient descent and backpropagation.



Mathematics Of Big Data

Mathematics of Big Data PDF
Author: Jeremy Kepner
Publisher: MIT Press
ISBN: 0262347911
Release: 2018-07-13
Size: 75.56 MB
Format: PDF
Category : Computers
Languages : en
Pages : 448
View: 4623

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Mathematics Of Big Data PDF Full

Linear Algebra Tools For Data Mining by Jeremy Kepner, Mathematics Of Big Data Books available in PDF, EPUB, Mobi Format. Download Mathematics Of Big Data books, The first book to present the common mathematical foundations of big data analysis across a range of applications and technologies. Today, the volume, velocity, and variety of data are increasing rapidly across a range of fields, including Internet search, healthcare, finance, social media, wireless devices, and cybersecurity. Indeed, these data are growing at a rate beyond our capacity to analyze them. The tools—including spreadsheets, databases, matrices, and graphs—developed to address this challenge all reflect the need to store and operate on data as whole sets rather than as individual elements. This book presents the common mathematical foundations of these data sets that apply across many applications and technologies. Associative arrays unify and simplify data, allowing readers to look past the differences among the various tools and leverage their mathematical similarities in order to solve the hardest big data challenges. The book first introduces the concept of the associative array in practical terms, presents the associative array manipulation system D4M (Dynamic Distributed Dimensional Data Model), and describes the application of associative arrays to graph analysis and machine learning. It provides a mathematically rigorous definition of associative arrays and describes the properties of associative arrays that arise from this definition. Finally, the book shows how concepts of linearity can be extended to encompass associative arrays. Mathematics of Big Data can be used as a textbook or reference by engineers, scientists, mathematicians, computer scientists, and software engineers who analyze big data.



When Life Is Linear

When Life is Linear PDF
Author: Timothy P. Chartier
Publisher:
ISBN: 9780883859889
Release: 2015
Size: 21.54 MB
Format: PDF, Docs
Category : MATHEMATICS
Languages : en
Pages : 151
View: 3355

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When Life Is Linear PDF Full

Linear Algebra Tools For Data Mining by Timothy P. Chartier, When Life Is Linear Books available in PDF, EPUB, Mobi Format. Download When Life Is Linear books,



Grouping Multidimensional Data

Grouping Multidimensional Data PDF
Author: Jacob Kogan
Publisher: Springer Science & Business Media
ISBN: 3540283498
Release: 2006-02-08
Size: 57.28 MB
Format: PDF, ePub, Docs
Category : Computers
Languages : en
Pages : 268
View: 1971

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Grouping Multidimensional Data PDF Full

Linear Algebra Tools For Data Mining by Jacob Kogan, Grouping Multidimensional Data Books available in PDF, EPUB, Mobi Format. Download Grouping Multidimensional Data books, Clustering is one of the most fundamental and essential data analysis techniques. Clustering can be used as an independent data mining task to discern intrinsic characteristics of data, or as a preprocessing step with the clustering results then used for classification, correlation analysis, or anomaly detection. Kogan and his co-editors have put together recent advances in clustering large and high-dimension data. Their volume addresses new topics and methods which are central to modern data analysis, with particular emphasis on linear algebra tools, opimization methods and statistical techniques. The contributions, written by leading researchers from both academia and industry, cover theoretical basics as well as application and evaluation of algorithms, and thus provide an excellent state-of-the-art overview. The level of detail, the breadth of coverage, and the comprehensive bibliography make this book a perfect fit for researchers and graduate students in data mining and in many other important related application areas.



Linear Algebra And Optimization For Machine Learning

Linear Algebra and Optimization for Machine Learning PDF
Author: Charu C. Aggarwal
Publisher: Springer Nature
ISBN: 3030403440
Release: 2020-05-13
Size: 62.35 MB
Format: PDF
Category : Computers
Languages : en
Pages : 495
View: 4212

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Linear Algebra And Optimization For Machine Learning PDF Full

Linear Algebra Tools For Data Mining by Charu C. Aggarwal, Linear Algebra And Optimization For Machine Learning Books available in PDF, EPUB, Mobi Format. Download Linear Algebra And Optimization For Machine Learning books, This textbook introduces linear algebra and optimization in the context of machine learning. Examples and exercises are provided throughout this text book together with access to a solution’s manual. This textbook targets graduate level students and professors in computer science, mathematics and data science. Advanced undergraduate students can also use this textbook. The chapters for this textbook are organized as follows: 1. Linear algebra and its applications: The chapters focus on the basics of linear algebra together with their common applications to singular value decomposition, matrix factorization, similarity matrices (kernel methods), and graph analysis. Numerous machine learning applications have been used as examples, such as spectral clustering, kernel-based classification, and outlier detection. The tight integration of linear algebra methods with examples from machine learning differentiates this book from generic volumes on linear algebra. The focus is clearly on the most relevant aspects of linear algebra for machine learning and to teach readers how to apply these concepts. 2. Optimization and its applications: Much of machine learning is posed as an optimization problem in which we try to maximize the accuracy of regression and classification models. The “parent problem” of optimization-centric machine learning is least-squares regression. Interestingly, this problem arises in both linear algebra and optimization, and is one of the key connecting problems of the two fields. Least-squares regression is also the starting point for support vector machines, logistic regression, and recommender systems. Furthermore, the methods for dimensionality reduction and matrix factorization also require the development of optimization methods. A general view of optimization in computational graphs is discussed together with its applications to back propagation in neural networks. A frequent challenge faced by beginners in machine learning is the extensive background required in linear algebra and optimization. One problem is that the existing linear algebra and optimization courses are not specific to machine learning; therefore, one would typically have to complete more course material than is necessary to pick up machine learning. Furthermore, certain types of ideas and tricks from optimization and linear algebra recur more frequently in machine learning than other application-centric settings. Therefore, there is significant value in developing a view of linear algebra and optimization that is better suited to the specific perspective of machine learning.



Mathematical Tools For Applied Multivariate Analysis

Mathematical Tools for Applied Multivariate Analysis PDF
Author: Paul E. Green
Publisher: Academic Press
ISBN: 1483214044
Release: 2014-05-10
Size: 30.34 MB
Format: PDF, Docs
Category : Mathematics
Languages : en
Pages : 402
View: 6719

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Mathematical Tools For Applied Multivariate Analysis PDF Full

Linear Algebra Tools For Data Mining by Paul E. Green, Mathematical Tools For Applied Multivariate Analysis Books available in PDF, EPUB, Mobi Format. Download Mathematical Tools For Applied Multivariate Analysis books, Mathematical Tools for Applied Multivariate Analysis provides information pertinent to the aspects of transformational geometry, matrix algebra, and the calculus that are most relevant for the study of multivariate analysis. This book discusses the mathematical foundations of applied multivariate analysis. Organized into six chapters, this book begins with an overview of the three problems in multiple regression, principal components analysis, and multiple discriminant analysis. This text then presents a standard treatment of the mechanics of matrix algebra, including definitions and operations on matrices, vectors, and determinants. Other chapters consider the topics of eigenstructures and linear transformations that are important to the understanding of multivariate techniques. This book discusses as well the eigenstructures and quadratic forms. The final chapter deals with the geometric aspects of linear transformations. This book is a valuable resource for students.