Articles by "Data Analysis"

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Showing posts with label Data Analysis. Show all posts

Xin-She Yang ... 175 pages - Publisher: Academic Press; (June, 2019) ... Language: English - ASIN: B07T7VSR37 by Amazon.

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.

Features: 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.

Bertrand Clarke, Ernest Fokoue, Hao Helen Zhang ... 786 pages - Publisher: Springer; (July, 2009) ... Language: English - ISBN-10: 0387981349 - ISBN-13: 978-0387981345. 

This book is a thorough introduction to the most important topics in data mining and machine learning. It begins with a detailed review of classical function estimation and proceeds with chapters on nonlinear regression, classification, and ensemble methods. The final chapters focus on clustering, dimension reduction, variable selection, and multiple comparisons. All these topics have undergone extraordinarily rapid development in recent years and this treatment offers a modern perspective emphasizing the most recent contributions. The presentation of foundational results is detailed and includes many accessible proofs not readily available outside original sources. While the orientation is conceptual and theoretical, the main points are regularly reinforced by computational comparisons.

Intended primarily as a graduate level textbook for statistics, computer science, and electrical engineering students, this book assumes only a strong foundation in undergraduate statistics and mathematics, and facility with using R packages. The text has a wide variety of problems, many of an exploratory nature. There are numerous computed examples, complete with code, so that further computations can be carried out readily. The book also serves as a handbook for researchers who want a conceptual overview of the central topics in data mining and machine learning.

Mehmed Kantardzic ... 672 pages - Publisher: Wiley-IEEE Press; 3rd edition (November, 2019) ... Language: English - ISBN-10: 1119516048 - ISBN-13: 978-1119516040. 

Presents the latest techniques for analyzing and extracting information from large amounts of data in high-dimensional data spaces: The revised and updated third edition of Data Mining contains in one volume an introduction to a systematic approach to the analysis of large data sets that integrates results from disciplines such as statistics, artificial intelligence, data bases, pattern recognition, and computer visualization. Advances in deep learning technology have opened an entire new spectrum of applications. The author―a noted expert on the topic―explains the basic concepts, models, and methodologies that have been developed in recent years. This new edition introduces and expands on many topics, as well as providing revised sections on software tools and data mining applications. Additional changes include an updated list of references for further study, and an extended list of problems and questions that relate to each chapter.This third edition presents new and expanded information that: • Explores big data and cloud computing • Examines deep learning • Includes information on convolutional neural networks (CNN) • Offers reinforcement learning • Contains semi-supervised learning and S3VM • Reviews model evaluation for unbalanced data. Written for graduate students in computer science, computer engineers, and computer information systems professionals, the updated third edition of Data Mining continues to provide an essential guide to the basic principles of the technology and the most recent developments in the field.

Bruce Ratner ... 724 pages - Publisher: Chapman and Hall/CRC; 3rd edition (June, 2017) ... Language: English - ISBN-10: 9781498797603 - ISBN-13: 978-1498797603. 

Interest in predictive analytics of big data has grown exponentially in the four years since the publication of Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data, Second Edition. In the third edition of this bestseller, the author has completely revised, reorganized, and repositioned the original chapters and produced 13 new chapters of creative and useful machine-learning data mining techniques. In sum, the 43 chapters of simple yet insightful quantitative techniques make this book unique in the field of data mining literature.

What is new in the Third Edition: The current chapters have been completely rewritten. + The core content has been extended with strategies and methods for problems drawn from the top predictive analytics conference and statistical modeling workshops. + Adds thirteen new chapters including coverage of data science and its rise, market share estimation, share of wallet modeling without survey data, latent market segmentation, statistical regression modeling that deals with incomplete data, decile analysis assessment in terms of the predictive power of the data, and a user-friendly version of text mining, not requiring an advanced background in natural language processing (NLP). + Includes SAS subroutines which can be easily converted to other languages. As in the previous edition, this book offers detailed background, discussion, and illustration of specific methods for solving the most commonly experienced problems in predictive modeling and analysis of big data. The author addresses each methodology and assigns its application to a specific type of problem. To better ground readers, the book provides an in-depth discussion of the basic methodologies of predictive modeling and analysis. While this type of overview has been attempted before, this approach offers a truly nitty-gritty, step-by-step method that both tyros and experts in the field can enjoy playing with.

January 09, 2020 , ,
Ying Tan, Yuhui Shi, Qirong Tang ... 820 pages - Publisher: Springer; (June, 2018) ... Language: English - ASIN: B07DMV9N6P by Amazon.

This book constitutes the refereed proceedings of the Third International Conference on Data Mining and Big Data, DMBD 2018, held in Shanghai, China, in June 2018. The 74 papers presented in this volume were carefully reviewed and selected from 126 submissions. They are organized in topical sections named: database, data preprocessing, matrix factorization, data analysis, visualization, visibility analysis, clustering, prediction, classification, pattern discovery, text mining and knowledge management, recommendation system in social media, deep learning, big data, Industry 4.0, practical applications

Zacharias Voulgaris, Yunus Emrah Bulut ... 312 pages - Publisher: Technics Publications; (September, 2018) ... Language: English - ASIN: B07HJDNZL2 by Amazon.

Aspiring and practicing Data Science and AI professionals, along with Python and Julia programmers, will practice numerous AI algorithms and develop a more holistic understanding of the field of AI, and will learn when to use each framework to tackle projects in our increasingly complex world.  

The first two chapters introduce the field, with Chapter 1 surveying Deep Learning models and Chapter 2 providing an overview of algorithms beyond Deep Learning, including Optimization, Fuzzy Logic, and Artificial Creativity. The next chapters focus on AI frameworks; they contain data and Python and Julia code in a provided Docker, so you can practice. Chapter 3 covers Apache's MXNet, Chapter 4 covers TensorFlow, and Chapter 5 investigates Keras. After covering these Deep Learning frameworks, we explore a series of optimization frameworks, with Chapter 6 covering Particle Swarm Optimization (PSO), Chapter 7 on Genetic Algorithms (GAs), and Chapter 8 discussing Simulated Annealing (SA). Chapter 9 begins our exploration of advanced AI methods, by covering Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). Chapter 10 discusses optimization ensembles and how they can add value to the Data Science pipeline. 
Chapter 11 contains several alternative AI frameworks including Extreme Learning Machines (ELMs), Capsule Networks (CapsNets), and Fuzzy Inference Systems (FIS). Chapter 12 covers other considerations complementary to the AI topics covered, including Big Data concepts, Data Science specialization areas, and useful data resources to experiment on.

A comprehensive glossary is included, as well as a series of appendices covering Transfer Learning, Reinforcement Learning, Autoencoder Systems, and Generative Adversarial Networks. There is also an appendix on the business aspects of AI in data science projects, and an appendix on how to use the Docker image to access the book's data and code. The field of AI is vast, and can be overwhelming for the newcomer to approach. This book will arm you with a solid understanding of the field, plus inspire you to explore further.

December 03, 2019 , ,
Bryan F.J. Manly, Jorge A. Navarro Alberto ... 269 pages - Publisher: Routledge; 4th edition (October, 2016) ... Language: English - ISBN-10: 1498728960 - ISBN-13: 978-1498728966.

Multivariate Statistical Methods: A Primer provides an introductory overview of multivariate methods without getting too deep into the mathematical details. This fourth edition is a revised and updated version of this bestselling introductory textbook. It retains the clear and concise style of the previous editions of the book and focuses on examples from biological and environmental sciences. The major update with this edition is that R code has been included for each of the analyses described, although in practice any standard statistical package can be used. The original idea with this book still applies. This was to make it as short as possible and enable readers to begin using multivariate methods in an intelligent manner. With updated information on multivariate analyses, new references, and R code included, this book continues to provide a timely introduction to useful tools for multivariate statistical analysis.

Wengang Zhang ... 240 pages - Publisher: Springer; (April, 2019) ... Language: English - ASIN: B07R93QW6S by Amazon.

This book presents the application of a comparatively simple nonparametric regression algorithm, known as the multivariate adaptive regression splines (MARS) surrogate model, which can be used to approximate the relationship between the inputs and outputs, and express that relationship mathematically. The book first describes the MARS algorithm, then highlights a number of geotechnical applications with multivariate big data sets to explore the approach’s generalization capabilities and accuracy. As such, it offers a valuable resource for all geotechnical researchers, engineers, and general readers interested in big data analysis.

November 06, 2019 , ,
Per Christian Hansen, Víctor Pereyra, Godela Scherer ... 324 pages - Publisher: JHUP; (December, 2012) ... Language: English - ASIN: B00AXYQU5M by Amazon.

As one of the classical statistical regression techniques, and often the first to be taught to new students, least squares fitting can be a very effective tool in data analysis. Given measured data, we establish a relationship between independent and dependent variables so that we can use the data predictively. The main concern of Least Squares Data Fitting with Applications is how to do this on a computer with efficient and robust computational methods for linear and nonlinear relationships. The presentation also establishes a link between the statistical setting and the computational issues. In a number of applications, the accuracy and efficiency of the least squares fit is central, and Per Christian Hansen, Víctor Pereyra, and Godela Scherer survey modern computational methods and illustrate them in fields ranging from engineering and environmental sciences to geophysics. Anyone working with problems of linear and nonlinear least squares fitting will find this book invaluable as a hands-on guide, with accessible text and carefully explained problems. Included are• an overview of computational methods together with their properties and advantages• topics from statistical regression analysis that help readers to understand and evaluate the computed solutions• many examples that illustrate the techniques and algorithms. Least Squares Data Fitting with Applications can be used as a textbook for advanced undergraduate or graduate courses and professionals in the sciences and in engineering.

October 25, 2019 , ,
Michael Z. Zgurovsky, Yuriy P. Zaychenko ... 304 pages - Publisher: Springer (May, 2020) ... Language: English - ISBN-10: 3030143007 - ISBN-13: 978-3030143008.

The book is devoted to the analysis of big data in order to extract from these data hidden patterns necessary for making decisions about the rational behavior of complex systems with the different nature that generate this data. To solve these problems, a group of new methods and tools is used, based on the self-organization of computational processes, the use of crisp and fuzzy cluster analysis methods, hybrid neural-fuzzy networks, and others. The book solves various practical problems. In particular, for the tasks of 3D image recognition and automatic speech recognition large-scale neural networks with applications for Deep Learning systems were used. Application of hybrid neuro-fuzzy networks for analyzing stock markets was presented. The analysis of big historical, economic and physical data revealed the hidden Fibonacci pattern about the course of systemic world conflicts and their connection with the Kondratieff big economic cycles and the Schwabe–Wolf solar activity cycles. The book is useful for system analysts and practitioners working with complex systems in various spheres of human activity.

October 23, 2019 , , ,
Olaf Wolkenhauer  ... 296 pages - Publisher: Wiley-Interscience; (July, 2001) ... Language: English - ISBN-10: 0471416568 - ISBN-13: 978-0471416562.

A survey of the philosophical implications and practical applications of fuzzy systems: Fuzzy mathematical concepts such as fuzzy sets, fuzzy logic, and similarity relations represent one of the most exciting currents in modern engineering and have great potential in applications ranging from control theory to bioinformatics. Data Engineering guides the reader through a number of concepts interconnected by fuzzy mathematics and discusses these concepts from a systems engineering perspective to showcase the continuing vitality, attractiveness, and applicability of fuzzy mathematics.

The author discusses the fundamental aspects of data analysis, systems modeling, and uncertainty calculi. He avoids a narrow discussion of specialized methodologies and takes a holistic view of the nature and application of fuzzy systems, considering principles, paradigms, and methodologies along the way. This broad coverage includes: * Fundamentals of modeling, identification, and clustering * System analysis * Uncertainty techniques * Random-set modeling and identification * Fuzzy inference engines * Fuzzy classification, control, and mathematics. In the important emerging field of bioinformatics, the book sets out how to encode a natural system in mathematical models, describes methods to identify interrelationships and interactions from data, and thereby helps the practitioner to decide which variables to measure and why. Data Engineering serves as an up-to-date and informative survey of the theoretical and practical tools for analyzing complex systems. It offers a unique treatment of complex issues that is accessible to students and researchers from a variety of backgrounds.

October 19, 2019 , , ,
Peter Bruce, Andrew Bruce ... 320 pages - Publisher: O'Reilly; (May, 2017) ... Language: English - ISBN-10: 1491952962 - ISBN-13: 978-1491952962. 

Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. With this book, you’ll learn: Why exploratory data analysis is a key preliminary step in data science - How random sampling can reduce bias and yield a higher quality dataset, even with big data - How the principles of experimental design yield definitive answers to questions - How to use regression to estimate outcomes and detect anomalies - Key classification techniques for predicting which categories a record belongs to - Statistical machine learning methods that “learn” from data - Unsupervised learning methods for extracting meaning from unlabeled data.

Ross Baldick ... 792 pages - Publisher: Cambridge University Press; (January, 2009) ... Language: English - ISBN-10: 0521100283 - ISBN-13: 978-0521100281.

The starting point in the formulation of any numerical problem is to take an intuitive idea about the problem in question and to translate it into precise mathematical language. This book provides step-by-step descriptions of how to formulate numerical problems so that they can be solved by existing software. It examines various types of numerical problems and develops techniques for solving them. A number of engineering case studies are used to illustrate in detail the formulation process. The case studies motivate the development of efficient algorithms that involve, in some cases, transformation of the problem from its initial formulation into a more tractable form.

Charles M. Judd, Gary H. McClelland, Carey S. Ryan ... 378 pages - Publisher: Routledge; 3rd edition (May, 2017) ... Language: English - ISBN-10: 1138819832 - ISBN-13: 978-1138819832.

Data Analysis: A Model Comparison Approach to Regression, ANOVA, and Beyond is an integrated treatment of data analysis for the social and behavioral sciences. It covers all of the statistical models normally used in such analyses, such as multiple regression and analysis of variance, but it does so in an integrated manner that relies on the comparison of models of data estimated under the rubric of the general linear model. Data Analysis also describes how the model comparison approach and uniform framework can be applied to models that include product predictors (i.e., interactions and nonlinear effects) and to observations that are nonindependent. Indeed, the analysis of nonindependent observations is treated in some detail, including models of nonindependent data with continuously varying predictors as well as standard repeated measures analysis of variance. This approach also provides an integrated introduction to multilevel or hierarchical linear models and logistic regression. Finally, Data Analysis provides guidance for the treatment of outliers and other problematic aspects of data analysis. It is intended for advanced undergraduate and graduate level courses in data analysis and offers an integrated approach that is very accessible and easy to teach.

Highlights of the third edition include: A new chapter on logistic regression; Expanded treatment of mixed models for data with multiple random factors; Updated examples; An enhanced website with PowerPoint presentations and other tools that demonstrate the concepts in the book; exercises for each chapter that highlight research findings from the literature; data sets, R code, and SAS output for all analyses; additional examples and problem sets; and test questions.

Wenyu Sun, Ya-Xiang Yuan ... 688 pages - Publisher: Springer; (May, 2006) ... Language: English - ISBN-10: 0387249753 - ISBN-13: 978-0387249759.

This book, a result of the author's teaching and research experience in various universities and institutes over the past ten years, can be used as a textbook for an optimization course for graduates and senior undergraduates. It systematically describes optimization theory and several powerful methods, including recent results. For most methods, the authors discuss an idea’s motivation, study the derivation, establish the global and local convergence, describe algorithmic steps, and discuss the numerical performance. The book deals with both theory and algorithms of optimization concurrently. It also contains an extensive bibliography. Finally, apart from its use for teaching, Optimization Theory and Methods will be very beneficial as a research reference.

September 27, 2019 , ,
John Wolberg ... 250 pages - Publisher: Springer Berlin Heidelberg; (February, 2006) ... Language: English - ASIN: B000VHULZG by Amazon.

The preferred method of data analysis of quantitative experiments is the method of least squares. Often, however, the full power of the method is overlooked and very few books deal with this subject at the level that it deserves. The purpose of Data Analysis Using the Method of Least Squares is to fill this gap and include the type of information required to help scientists and engineers apply the method to problems in their special fields of interest. In addition, graduate students in science and engineering doing work of experimental nature can benefit from this book. Particularly, both linear and non-linear least squares, the use of experimental error estimates for data weighting, procedures to include prior estimates, methodology for selecting and testing models, prediction analysis, and some non-parametric methods are discussed.

Erik Cuevas, Daniel Zaldívar, Marco Pérez-Cisneros ... 218 pages - Publisher: Springer; (April, 2018) ... Language: English - ASIN: B07C3LK4K3 by Amazon.

This book explores new alternative metaheuristic developments that have proved to be effective in their application to several complex problems. Though most of the new metaheuristic algorithms considered offer promising results, they are nevertheless still in their infancy. To grow and attain their full potential, new metaheuristic methods must be applied in a great variety of problems and contexts, so that they not only perform well in their reported sets of optimization problems, but also in new complex formulations. The only way to accomplish this is to disseminate these methods in various technical areas as optimization tools. In general, once a scientist, engineer or practitioner recognizes a problem as a particular instance of a more generic class, he/she can select one of several metaheuristic algorithms that guarantee an expected optimization performance. Unfortunately, the set of options are concentrated on algorithms whose popularity and high proliferation outstrip those of the new developments. This structure is important, because the authors recognize this methodology as the best way to help researchers, lecturers, engineers and practitioners solve their own optimization problems.

Andreas Müller, Sarah Guido ... 400 pages - Publisher: O'Reilly Media; (October, 2016) ... Language: English - ISBN-10: 1449369413 - ISBN-13: 978-1449369415.

Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination. You’ll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Müller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book.

With this book, you’ll learn: Fundamental concepts and applications of machine learning + Advantages and shortcomings of widely used machine learning algorithms + How to represent data processed by machine learning, including which data aspects to focus on + Advanced methods for model evaluation and parameter tuning + The concept of pipelines for chaining models and encapsulating your workflow + Methods for working with text data, including text-specific processing techniques + Suggestions for improving your machine learning and data science skills.

September 19, 2019 , , , ,
MoonJung Cho, Wendy L. Martinez ... 286 pages - Publisher: Chapman and Hall/CRC; (December, 2014) ... Language: English - ISBN-10: 1466596562 - ISBN-13: 978-1466596566

Fulfilling the need for a practical user’s guide, Statistics in MATLAB: A Primer provides an accessible introduction to the latest version of MATLAB and its extensive functionality for statistics. Assuming a basic knowledge of statistics and probability as well as a fundamental understanding of linear algebra concepts, this book: Covers capabilities in the main MATLAB package, the Statistics Toolbox, and the student version of MATLAB * Presents examples of how MATLAB can be used to analyze data * Offers access to a companion website with data sets and additional examples * Contains figures and visual aids to assist in application of the software *Explains how to determine what method should be used for analysis. Statistics in MATLAB: A Primer is an ideal reference for undergraduate and graduate students in engineering, mathematics, statistics, economics, biostatistics, and computer science. It is also appropriate for a diverse professional market, making it a valuable addition to the libraries of researchers in statistics, computer science, data mining, machine learning, image analysis, signal processing, and engineering.

September 16, 2019 , ,
Wendy L. Martinez, Angel R. Martinez, Jeffrey L. Solka ... 616 pages - Publisher: Chapman and Hall/CRC; 3rd edition (August, 2017) ... Language: English - ASIN: B074MVTKM9 by Amazon

Exploratory Data Analysis (EDA) is an important part of the data analysis process. The methods presented in this text are ones that should be in the toolkit of every data scientist. As computational sophistication has increased and data sets have grown in size and complexity, EDA has become an even more important process for visualizing and summarizing data before making assumptions to generate hypotheses and models. Exploratory Data Analysis with MATLAB, Third Edition presents EDA methods from a computational perspective and uses numerous examples and applications to show how the methods are used in practice. The authors use MATLAB code, pseudo-code, and algorithm descriptions to illustrate the concepts. The MATLAB code for examples, data sets, and the EDA Toolbox are available for download on the book’s website.

New to the Third Edition: Random projections and estimating local intrinsic dimensionality. + Deep learning autoencoders and stochastic neighbor embedding. + Minimum spanning tree and additional cluster validity indices. + Kernel density estimation. + Plots for visualizing data distributions, such as beanplots and violin plots. + A chapter on visualizing categorical data.

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