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Outlier Analysis, Charu C. Aggarwal


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Автор: Charu C. Aggarwal
Название:  Outlier Analysis
ISBN: 9783319475776
Издательство: Springer
Классификация:


ISBN-10: 3319475770
Обложка/Формат: Hardback
Страницы: 466
Вес: 1.11 кг.
Дата издания: 2016
Серия: Computer Science
Язык: English
Издание: 2 rev ed
Иллюстрации: 65 black & white illustrations, 13 colour illustrations, 10 colour tables, biography
Размер: 188 x 265 x 33
Читательская аудитория: Graduate/advanced undergraduate textbook
Ссылка на Издательство: Link
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Поставляется из: Германии
Описание:
This book provides comprehensive coverage of the field of outlier analysis from a computer science point of view. It integrates methods from data mining, machine learning, and statistics within the computational framework and therefore appeals to multiple communities. The chapters of this book can be organized into three categories:
Basic algorithms: Chapters 1 through 7 discuss the fundamental algorithms for outlier analysis, including probabilistic and statistical methods, linear methods, proximity-based methods, high-dimensional (subspace) methods, ensemble methods, and supervised methods.Domain-specific methods: Chapters 8 through 12 discuss outlier detection algorithms for various domains of data, such as text, categorical data, time-series data, discrete sequence data, spatial data, and network data.Applications: Chapter 13 is devoted to various applications of outlier analysis. Some guidance is also provided for the practitioner.
The second edition of this book is more detailed and is written to appeal to both researchers and practitioners. Significant new material has been added on topics such as kernel methods, one-class support-vector machines, matrix factorization, neural networks, outlier ensembles, time-series methods, and subspace methods. It is written as a textbook and can be used for classroom teaching.

Дополнительное описание: An Introduction to Outlier Analysis.- Probabilistic Models for Outlier Detection.- Linear Models for Outlier Detection.- Proximity-Based Outlier Detection.- High-Dimension Outlier Detection.- Outlier Ensembles.- Supervised Outlier Detection.- Categorical,



Bayesian Evolutionary Analysis with BEAST

Автор: Drummond
Название: Bayesian Evolutionary Analysis with BEAST
ISBN: 1107019656 ISBN-13(EAN): 9781107019652
Издательство: Cambridge Academ
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Цена: 8395.00 р.
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Описание: A comprehensive overview of Bayesian approaches to phylogenetics using Markov chain Monte Carlo (MCMC) methods, offering theoretical insight, pragmatic advice and tools to develop further models. This title is a one-stop reference to applying the latest phylogenetic models in BEAST 2, interpreting the analyses and extending these models further.

Numerical Analysis Using R

Автор: Griffiths
Название: Numerical Analysis Using R
ISBN: 1107115612 ISBN-13(EAN): 9781107115613
Издательство: Cambridge Academ
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Цена: 11722.00 р.
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Описание: This book presents the latest numerical solutions to initial value problems and boundary value problems described by ODEs and PDEs. The author offers practical methods for a wide range of problems and illustrates them in the increasingly popular open source language R, allowing integration with more statistical methods.

Graphical Data Analysis with R

Автор: Unwin
Название: Graphical Data Analysis with R
ISBN: 1498715230 ISBN-13(EAN): 9781498715232
Издательство: Taylor&Francis
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Цена: 11482.00 р.
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Описание:

See How Graphics Reveal Information

Graphical Data Analysis with R shows you what information you can gain from graphical displays. The book focuses on why you draw graphics to display data and which graphics to draw (and uses R to do so). All the datasets are available in R or one of its packages and the R code is available at rosuda.org/GDA.

Graphical data analysis is useful for data cleaning, exploring data structure, detecting outliers and unusual groups, identifying trends and clusters, spotting local patterns, evaluating modelling output, and presenting results. This book guides you in choosing graphics and understanding what information you can glean from them. It can be used as a primary text in a graphical data analysis course or as a supplement in a statistics course. Colour graphics are used throughout.

Introduction to Scientific Programming and Simulation Using R, Second Edition

Автор: Jones
Название: Introduction to Scientific Programming and Simulation Using R, Second Edition
ISBN: 1466569999 ISBN-13(EAN): 9781466569997
Издательство: Taylor&Francis
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Цена: 12707.00 р.
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Описание:

Learn How to Program Stochastic Models

Highly recommended, the best-selling first edition of Introduction to Scientific Programming and Simulation Using R was lauded as an excellent, easy-to-read introduction with extensive examples and exercises. This second edition continues to introduce scientific programming and stochastic modelling in a clear, practical, and thorough way. Readers learn programming by experimenting with the provided R code and data.

The book's four parts teach:

  • Core knowledge of R and programming concepts
  • How to think about mathematics from a numerical point of view, including the application of these concepts to root finding, numerical integration, and optimisation
  • Essentials of probability, random variables, and expectation required to understand simulation
  • Stochastic modelling and simulation, including random number generation and Monte Carlo integration

In a new chapter on systems of ordinary differential equations (ODEs), the authors cover the Euler, midpoint, and fourth-order Runge-Kutta (RK4) schemes for solving systems of first-order ODEs. They compare the numerical efficiency of the different schemes experimentally and show how to improve the RK4 scheme by using an adaptive step size.

Another new chapter focuses on both discrete- and continuous-time Markov chains. It describes transition and rate matrices, classification of states, limiting behaviour, Kolmogorov forward and backward equations, finite absorbing chains, and expected hitting times. It also presents methods for simulating discrete- and continuous-time chains as well as techniques for defining the state space, including lumping states and supplementary variables.

Building readers' statistical intuition, Introduction to Scientific Programming and Simulation Using R, Second Edition shows how to turn algorithms into code. It is designed for those who want to make tools, not just use them. The code and data are available for download from CRAN.

Using R and RStudio for Data Management, Statistical Analysis, and Graphics, Second Edition

Автор: Nicholas J. Horton , Ken Kleinman
Название: Using R and RStudio for Data Management, Statistical Analysis, and Graphics, Second Edition
ISBN: 1482237369 ISBN-13(EAN): 9781482237368
Издательство: Taylor&Francis
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Цена: 11789.00 р.
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Описание:

Improve Your Analytical Skills

Incorporating the latest R packages as well as new case studies and applications, Using R and RStudio for Data Management, Statistical Analysis, and Graphics, Second Edition covers the aspects of R most often used by statistical analysts. New users of R will find the book's simple approach easy to understand while more sophisticated users will appreciate the invaluable source of task-oriented information.

New to the Second Edition

  • The use of RStudio, which increases the productivity of R users and helps users avoid error-prone cut-and-paste workflows
  • New chapter of case studies illustrating examples of useful data management tasks, reading complex files, making and annotating maps, "scraping" data from the web, mining text files, and generating dynamic graphics
  • New chapter on special topics that describes key features, such as processing by group, and explores important areas of statistics, including Bayesian methods, propensity scores, and bootstrapping
  • New chapter on simulation that includes examples of data generated from complex models and distributions
  • A detailed discussion of the philosophy and use of the knitr and markdown packages for R
  • New packages that extend the functionality of R and facilitate sophisticated analyses
  • Reorganized and enhanced chapters on data input and output, data management, statistical and mathematical functions, programming, high-level graphics plots, and the customization of plots

Easily Find Your Desired Task

Conveniently organized by short, clear descriptive entries, this edition continues to show users how to easily perform an analytical task in R. Users can quickly find and implement the material they need through the extensive indexing, cross-referencing, and worked examples in the text. Datasets and code are available for download on a supplementary website.

Big and Complex Data Analysis

Автор: Ahmed
Название: Big and Complex Data Analysis
ISBN: 3319415727 ISBN-13(EAN): 9783319415727
Издательство: Springer
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Цена: 15372.00 р.
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Описание: This volume conveys some of the surprises, puzzles and success stories in high-dimensional and complex data analysis and related fields. Examples include epigenomic data, genomic data, proteomic data, high-resolution image data, high-frequency financial data, functional and longitudinal data, and network data.

Multiple Factor Analysis by Example Using R

Автор: Pages
Название: Multiple Factor Analysis by Example Using R
ISBN: 1482205475 ISBN-13(EAN): 9781482205473
Издательство: Taylor&Francis
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Цена: 13014.00 р.
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Описание: Multiple factor analysis (MFA) enables users to analyze tables of individuals and variables in which the variables are structured into quantitative, qualitative, or mixed groups. Written by the co-developer of this methodology, Multiple Factor Analysis by Example Using R brings together the theoretical and methodological aspects of MFA. It also includes examples of applications and details of how to implement MFA using an R package (FactoMineR). The first two chapters cover the basic factorial analysis methods of principal component analysis (PCA) and multiple correspondence analysis (MCA). The next chapter discusses factor analysis for mixed data (FAMD), a little-known method for simultaneously analyzing quantitative and qualitative variables without group distinction. Focusing on MFA, subsequent chapters examine the key points of MFA in the context of quantitative variables as well as qualitative and mixed data. The author also compares MFA and Procrustes analysis and presents a natural extension of MFA: hierarchical MFA (HMFA). The final chapter explores several elements of matrix calculation and metric spaces used in the book.

Review Comment Analysis For E-Commerce

Автор: Zhang Rong Et Al
Название: Review Comment Analysis For E-Commerce
ISBN: 9813100044 ISBN-13(EAN): 9789813100046
Издательство: World Scientific Publishing
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Цена: 11880.00 р.
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Описание: This book presents the recent achievements on the processing of representative user generated content (UGC) on E-commerce websites. This large size of UGC is valuable information for data mining to help customer/object profiling. It provides a comprehensive overview on the concept of customer credibility, object-oriented review summarization technology and content-based collaborative filtering algorithm. It covers a feedback mechanism which is designed to discover customer credibility, which is used to define the professional degree of review content; product-oriented review summarization for restaurants or trip arrangements, and introduced content-based collaborative filtering for product recommendation.

R for Programmers

Автор: Zhang
Название: R for Programmers
ISBN: 1498736815 ISBN-13(EAN): 9781498736817
Издательство: Taylor&Francis
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Цена: 9492.00 р.
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Описание:

Unlike other books about R, written from the perspective of statistics, R for Programmers: Mastering the Tools is written from the perspective of programmers, providing a channel for programmers with expertise in other programming languages to quickly understand R. The contents are divided into four sections: The first section consists of the basics of R, which explains the advantages of using R, the installation of different versions of R, and the 12 frequently used packages of R. This will help you understand the tool packages, time series packages, and performance monitoring packages of R quickly.

The second section discusses the server of R, which examines the communication between R and other programming languages and the application of R as servers. This will help you integrate R with other programming languages and implement the server application of R. The third section discusses databases and big data, which covers the communication between R and various databases, as well as R's integration with Hadoop. This will help you integrate R with the underlying level of other databases and implement the processing of big data by R, based on Hadoop.

The fourth section comprises the appendices, which introduce the installation of Java, various databases, and Hadoop. Because this is a reference book, there is no special sequence for reading all the chapters. You can choose the chapters in which you have an interest. If you are new to R, and you wish to master R comprehensively, simply follow the chapters in sequence.

Design and Analysis of Experiments with R

Автор: Lawson
Название: Design and Analysis of Experiments with R
ISBN: 1439868131 ISBN-13(EAN): 9781439868133
Издательство: Taylor&Francis
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Цена: 17609.00 р.
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Описание:

Design and Analysis of Experiments with R presents a unified treatment of experimental designs and design concepts commonly used in practice. It connects the objectives of research to the type of experimental design required, describes the process of creating the design and collecting the data, shows how to perform the proper analysis of the data, and illustrates the interpretation of results.

Drawing on his many years of working in the pharmaceutical, agricultural, industrial chemicals, and machinery industries, the author teaches students how to:

  • Make an appropriate design choice based on the objectives of a research project
  • Create a design and perform an experiment
  • Interpret the results of computer data analysis

The book emphasizes the connection among the experimental units, the way treatments are randomized to experimental units, and the proper error term for data analysis. R code is used to create and analyze all the example experiments. The code examples from the text are available for download on the author's website, enabling students to duplicate all the designs and data analysis.

Intended for a one-semester or two-quarter course on experimental design, this text covers classical ideas in experimental design as well as the latest research topics. It gives students practical guidance on using R to analyze experimental data.

Developing Churn Models Using Data Mining Techniques And Social Network Analysis

Автор: Klepac, Kopal & Mrsic
Название: Developing Churn Models Using Data Mining Techniques And Social Network Analysis
ISBN: 1466662883 ISBN-13(EAN): 9781466662889
Издательство: Mare Nostrum (Eurospan)
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Цена: 27027.00 р.
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Описание: Churn prediction, recognition, and mitigation have become essential topics in various industries. As a means for forecasting and manageing risk, further research in this field can greatly assist companies in making informed decisions based on future possible scenarios.Developing Churn Models Using Data Mining Techniques and Social Network Analysis provides an in-depth analysis of attrition modeling relevant to business planning and management. Through its insightful and detailed explanation of best practices, tools, and theory surrounding churn prediction and the integration of analytics tools, this publication is especially relevant to managers, data specialists, business analysts, academicians, and upper-level students.

Advanced Malware Analysis

Автор: Elisan Christopher
Название: Advanced Malware Analysis
ISBN: 0071819746 ISBN-13(EAN): 9780071819749
Издательство: McGraw-Hill
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Цена: 12353.00 р.
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Описание: A one-of-a-kind guide to setting up a state-of-the-art malware research lab, using and customizing dozens of cutting-edge analysis tools, and generating metrics-rich reports to document the findings


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