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Matrix Methods in Data Mining and Pattern Recognition, Second Edition

2019-08-30 Mathematics
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Release : 2019-08-30
Genre : Mathematics
Kind : eBook
Book Rating : 867/5 ( reviews)

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Book Synopsis Matrix Methods in Data Mining and Pattern Recognition, Second Edition by : Lars Elden

Download or read book Matrix Methods in Data Mining and Pattern Recognition, Second Edition written by Lars Elden. This book was released on 2019-08-30. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Data Mining with R

2016-11-30 Business & Economics
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Release : 2016-11-30
Genre : Business & Economics
Kind : eBook
Book Rating : 091/5 ( reviews)

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Book Synopsis Data Mining with R by : Luis Torgo

Download or read book Data Mining with R written by Luis Torgo. This book was released on 2016-11-30. Available in PDF, EPUB and Kindle. Book excerpt: Data Mining with R: Learning with Case Studies, Second Edition uses practical examples to illustrate the power of R and data mining. Providing an extensive update to the best-selling first edition, this new edition is divided into two parts. The first part will feature introductory material, including a new chapter that provides an introduction to data mining, to complement the already existing introduction to R. The second part includes case studies, and the new edition strongly revises the R code of the case studies making it more up-to-date with recent packages that have emerged in R. The book does not assume any prior knowledge about R. Readers who are new to R and data mining should be able to follow the case studies, and they are designed to be self-contained so the reader can start anywhere in the document. The book is accompanied by a set of freely available R source files that can be obtained at the book’s web site. These files include all the code used in the case studies, and they facilitate the "do-it-yourself" approach followed in the book. Designed for users of data analysis tools, as well as researchers and developers, the book should be useful for anyone interested in entering the "world" of R and data mining. About the Author Luís Torgo is an associate professor in the Department of Computer Science at the University of Porto in Portugal. He teaches Data Mining in R in the NYU Stern School of Business’ MS in Business Analytics program. An active researcher in machine learning and data mining for more than 20 years, Dr. Torgo is also a researcher in the Laboratory of Artificial Intelligence and Data Analysis (LIAAD) of INESC Porto LA.

Modern Structural Analysis

1991 Technology & Engineering
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Release : 1991
Genre : Technology & Engineering
Kind : eBook
Book Rating : /5 ( reviews)

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Book Synopsis Modern Structural Analysis by : Anthony E. Armenàkas

Download or read book Modern Structural Analysis written by Anthony E. Armenàkas. This book was released on 1991. Available in PDF, EPUB and Kindle. Book excerpt: This companion to the previously published book [BO]Classical Structural Analysis[BX], also by the same author, focuses on advanced structural analysis using matrix methods for the element method of design calculations. With this method, the structural properties of each structural member (or element) taken together, of an entire structure, are used to calculate load behaviour and construction needs of a whole building or other structure. The matrix method is particularly suited to computer methods that must employ thousands of reiterate calculations. The book contains dozens of worked-out problems and design exercises, as well as an actual computer program at the end of the book for matrix method calculations.

Data Clustering: Theory, Algorithms, and Applications, Second Edition

2020-11-10 Mathematics
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Release : 2020-11-10
Genre : Mathematics
Kind : eBook
Book Rating : 332/5 ( reviews)

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Book Synopsis Data Clustering: Theory, Algorithms, and Applications, Second Edition by : Guojun Gan

Download or read book Data Clustering: Theory, Algorithms, and Applications, Second Edition written by Guojun Gan. This book was released on 2020-11-10. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Pattern Recognition and Machine Learning

2016-08-23 Computers
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Release : 2016-08-23
Genre : Computers
Kind : eBook
Book Rating : 438/5 ( reviews)

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Book Synopsis Pattern Recognition and Machine Learning by : Christopher M. Bishop

Download or read book Pattern Recognition and Machine Learning written by Christopher M. Bishop. This book was released on 2016-08-23. Available in PDF, EPUB and Kindle. Book excerpt: This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

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