Описание: Exploratory data analysis (EDA) is about detecting and describing patterns, trends, and relations in data, motivated by certain purposes of investigation. The authors describe in detail and systemize approaches, techniques, and methods for exploring spatial and temporal data in particular.
Описание: The first textbook on social network analysis integrating theory, applications, and professional software.
Автор: Michel Jambu Название: Exploratory and Multivariate Data Analysis, ISBN: 0123800900 ISBN-13(EAN): 9780123800909 Издательство: Elsevier Science Рейтинг: Цена: 8160 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: With an index of notations at the beginning, this book explains the theory and application of data analysis methods from univariate to multidimensional and how to learn and use them efficiently. It also features a description of correspondence analysis and factor analysis techniques as multidimensional statistical data analysis techniques.
Описание: Originally published in hardcover in 1982, this book is now offered in a Wiley Classics Library edition. A contributed volume, edited by some of the preeminent statisticians of the 20th century, Understanding of Robust and Exploratory Data Analysis explains why and how to use exploratory data analysis and robust and resistant methods in statistical practice.
Описание: Like its best-selling predecessor, this second edition continues to use numerous examples and applications to show how EDA methods are used in practice. This edition covers many innovative approaches for dimensionality reduction, clustering, and visualization, including nonnegative matrix factorization, linear discriminant analysis, curvilinear component analysis, independent component analysis, smoothing splines, probabilistic latent semantic analysis, spectral-based clustering, a rangefinder boxplot, scatterplots with marginal histograms, biplots, and Andrews’ images method. MATLAB® codes are available for download on the book’s website.
Описание: Full of real-world case studies and practical advice, this book focuses on four fundamental methods of multivariate exploratory data analysis that are most suitable for applications. It covers principal component analysis when variables are quantitative, correspondence analysis and multiple correspondence analysis when variables are categorical, and hierarchical cluster analysis. The authors take a geometric point of view that provides a unified vision for exploring multivariate data tables. All of the data sets and code are available on the book’s website.
Описание: A review of standard algorithms provides the basis for more complex data mining techniques in this overview of exploratory data analysis. Recent reinforcement learning research is presented, as well as novel methods of parameter adaptation in machine learning.
Описание: Review of Clustering Algorithms.- Review of Linear Projection Methods.- Non-standard Clustering Criteria.- Topographic Mappings and Kernel Clustering.- Online Clustering Algorithms and Reinforcement Learning.- Connectivity Graphs and Clustering with Similarity Functions.- Reinforcement Learning of Projections.- Cross Entropy Methods.- Artificial Immune Systems.- Conclusions.
Автор: Pearson Название: Exploratory Data Analysis Using R ISBN: 149873023X ISBN-13(EAN): 9781498730235 Издательство: Taylor&Francis Рейтинг: Цена: 10344 р. Наличие на складе: Поставка под заказ.
Описание: Exploratory Data Analysis Using R provides a classroom-tested introduction to exploratory data analysis (EDA) and introduces the range of "interesting" – good, bad, and ugly – features that can be found in data, and why it is important to find them. It also introduces the mechanics of using R to explore and explain data. The book begins with a detailed overview of data, exploratory analysis, and R, as well as graphics in R. It then explores working with external data, linear regression models, and crafting data stories. The second part of the book focuses on developing R programs, including good programming practices and examples, working with text data, and general predictive models. The book ends with a chapter on "keeping it all together" that includes managing the R installation, managing files, documenting, and an introduction to reproducible computing. The book is designed for both advanced undergraduate, entry-level graduate students, and working professionals with little to no prior exposure to data analysis, modeling, statistics, or programming. it keeps the treatment relatively non-mathematical, even though data analysis is an inherently mathematical subject. Exercises are included at the end of most chapters, and an instructor's solution manual is available. About the Author: Ronald K. Pearson holds the position of Senior Data Scientist with GeoVera, a property insurance company in Fairfield, California, and he has previously held similar positions in a variety of application areas, including software development, drug safety data analysis, and the analysis of industrial process data. He holds a PhD in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology and has published conference and journal papers on topics ranging from nonlinear dynamic model structure selection to the problems of disguised missing data in predictive modeling. Dr. Pearson has authored or co-authored books including Exploring Data in Engineering, the Sciences, and Medicine (Oxford University Press, 2011) and Nonlinear Digital Filtering with Python. He is also the developer of the DataCamp course on base R graphics and is an author of the datarobot and GoodmanKruskal R packages available from CRAN (the Comprehensive R Archive Network).
Описание: Praise for the Second Edition:"The authors present an intuitive and easy-to-read book. … accompanied by many examples, proposed exercises, good references, and comprehensive appendices that initiate the reader unfamiliar with MATLAB."—Adolfo Alvarez Pinto, International Statistical Review "Practitioners of EDA who use MATLAB will want a copy of this book. … The authors have done a great service by bringing together so many EDA routines, but their main accomplishment in this dynamic text is providing the understanding and tools to do EDA. —David A Huckaby, MAA Reviews 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
Full of real-world case studies and practical advice, Exploratory Multivariate Analysis by Example Using R, Second Edition focuses on four fundamental methods of multivariate exploratory data analysis that are most suitable for applications. It covers principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) and multiple correspondence analysis (MCA) when variables are categorical, and hierarchical cluster analysis.
The authors take a geometric point of view that provides a unified vision for exploring multivariate data tables. Within this framework, they present the principles, indicators, and ways of representing and visualising objects that are common to the exploratory methods. The authors show how to use categorical variables in a PCA context in which variables are quantitative, how to handle more than two categorical variables in a CA context in which there are originally two variables, and how to add quantitative variables in an MCA context in which variables are categorical. They also illustrate the methods using examples from various fields, with related R code accessible in the FactoMineR package developed by the authors.
The book has been written using minimal mathematics so as to appeal to applied statisticians, as well as researchers from various disciplines, including medical research and the social sciences. Readers can use the theory, examples, and software presented in this book in order to be fully equipped to tackle real-life multivariate data.
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