An Introduction To Clustering With R

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An Introduction to Clustering with R

Author : Paolo Giordani
Publisher : Springer Nature
ISBN 13 : 9811305536
Total Pages : 340 pages
Book Rating : 4.8/5 (113 download)

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Book Synopsis An Introduction to Clustering with R by : Paolo Giordani

Download or read book An Introduction to Clustering with R written by Paolo Giordani and published by Springer Nature. This book was released on 2020-08-27 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: The purpose of this book is to thoroughly prepare the reader for applied research in clustering. Cluster analysis comprises a class of statistical techniques for classifying multivariate data into groups or clusters based on their similar features. Clustering is nowadays widely used in several domains of research, such as social sciences, psychology, and marketing, highlighting its multidisciplinary nature. This book provides an accessible and comprehensive introduction to clustering and offers practical guidelines for applying clustering tools by carefully chosen real-life datasets and extensive data analyses. The procedures addressed in this book include traditional hard clustering methods and up-to-date developments in soft clustering. Attention is paid to practical examples and applications through the open source statistical software R. Commented R code and output for conducting, step by step, complete cluster analyses are available. The book is intended for researchers interested in applying clustering methods. Basic notions on theoretical issues and on R are provided so that professionals as well as novices with little or no background in the subject will benefit from the book.

Practical Guide to Cluster Analysis in R

Author : Alboukadel Kassambara
Publisher : STHDA
ISBN 13 : 1542462703
Total Pages : 187 pages
Book Rating : 4.5/5 (424 download)

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Book Synopsis Practical Guide to Cluster Analysis in R by : Alboukadel Kassambara

Download or read book Practical Guide to Cluster Analysis in R written by Alboukadel Kassambara and published by STHDA. This book was released on 2017-08-23 with total page 187 pages. Available in PDF, EPUB and Kindle. Book excerpt: Although there are several good books on unsupervised machine learning, we felt that many of them are too theoretical. This book provides practical guide to cluster analysis, elegant visualization and interpretation. It contains 5 parts. Part I provides a quick introduction to R and presents required R packages, as well as, data formats and dissimilarity measures for cluster analysis and visualization. Part II covers partitioning clustering methods, which subdivide the data sets into a set of k groups, where k is the number of groups pre-specified by the analyst. Partitioning clustering approaches include: K-means, K-Medoids (PAM) and CLARA algorithms. In Part III, we consider hierarchical clustering method, which is an alternative approach to partitioning clustering. The result of hierarchical clustering is a tree-based representation of the objects called dendrogram. In this part, we describe how to compute, visualize, interpret and compare dendrograms. Part IV describes clustering validation and evaluation strategies, which consists of measuring the goodness of clustering results. Among the chapters covered here, there are: Assessing clustering tendency, Determining the optimal number of clusters, Cluster validation statistics, Choosing the best clustering algorithms and Computing p-value for hierarchical clustering. Part V presents advanced clustering methods, including: Hierarchical k-means clustering, Fuzzy clustering, Model-based clustering and Density-based clustering.

Model-Based Clustering and Classification for Data Science

Author : Charles Bouveyron
Publisher : Cambridge University Press
ISBN 13 : 1108640591
Total Pages : 447 pages
Book Rating : 4.1/5 (86 download)

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Book Synopsis Model-Based Clustering and Classification for Data Science by : Charles Bouveyron

Download or read book Model-Based Clustering and Classification for Data Science written by Charles Bouveyron and published by Cambridge University Press. This book was released on 2019-07-25 with total page 447 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cluster analysis finds groups in data automatically. Most methods have been heuristic and leave open such central questions as: how many clusters are there? Which method should I use? How should I handle outliers? Classification assigns new observations to groups given previously classified observations, and also has open questions about parameter tuning, robustness and uncertainty assessment. This book frames cluster analysis and classification in terms of statistical models, thus yielding principled estimation, testing and prediction methods, and sound answers to the central questions. It builds the basic ideas in an accessible but rigorous way, with extensive data examples and R code; describes modern approaches to high-dimensional data and networks; and explains such recent advances as Bayesian regularization, non-Gaussian model-based clustering, cluster merging, variable selection, semi-supervised and robust classification, clustering of functional data, text and images, and co-clustering. Written for advanced undergraduates in data science, as well as researchers and practitioners, it assumes basic knowledge of multivariate calculus, linear algebra, probability and statistics.

Clustering

Author : Rui Xu
Publisher : John Wiley & Sons
ISBN 13 : 0470382783
Total Pages : 400 pages
Book Rating : 4.4/5 (73 download)

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Book Synopsis Clustering by : Rui Xu

Download or read book Clustering written by Rui Xu and published by John Wiley & Sons. This book was released on 2008-11-03 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the first book to take a truly comprehensive look at clustering. It begins with an introduction to cluster analysis and goes on to explore: proximity measures; hierarchical clustering; partition clustering; neural network-based clustering; kernel-based clustering; sequential data clustering; large-scale data clustering; data visualization and high-dimensional data clustering; and cluster validation. The authors assume no previous background in clustering and their generous inclusion of examples and references help make the subject matter comprehensible for readers of varying levels and backgrounds.

Applied Unsupervised Learning with R

Author : Alok Malik
Publisher : Packt Publishing Ltd
ISBN 13 : 1789951461
Total Pages : 320 pages
Book Rating : 4.7/5 (899 download)

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Book Synopsis Applied Unsupervised Learning with R by : Alok Malik

Download or read book Applied Unsupervised Learning with R written by Alok Malik and published by Packt Publishing Ltd. This book was released on 2019-03-27 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: Design clever algorithms that discover hidden patterns and draw responses from unstructured, unlabeled data. Key FeaturesBuild state-of-the-art algorithms that can solve your business' problemsLearn how to find hidden patterns in your dataRevise key concepts with hands-on exercises using real-world datasetsBook Description Starting with the basics, Applied Unsupervised Learning with R explains clustering methods, distribution analysis, data encoders, and features of R that enable you to understand your data better and get answers to your most pressing business questions. This book begins with the most important and commonly used method for unsupervised learning - clustering - and explains the three main clustering algorithms - k-means, divisive, and agglomerative. Following this, you'll study market basket analysis, kernel density estimation, principal component analysis, and anomaly detection. You'll be introduced to these methods using code written in R, with further instructions on how to work with, edit, and improve R code. To help you gain a practical understanding, the book also features useful tips on applying these methods to real business problems, including market segmentation and fraud detection. By working through interesting activities, you'll explore data encoders and latent variable models. By the end of this book, you will have a better understanding of different anomaly detection methods, such as outlier detection, Mahalanobis distances, and contextual and collective anomaly detection. What you will learnImplement clustering methods such as k-means, agglomerative, and divisiveWrite code in R to analyze market segmentation and consumer behaviorEstimate distribution and probabilities of different outcomesImplement dimension reduction using principal component analysisApply anomaly detection methods to identify fraudDesign algorithms with R and learn how to edit or improve codeWho this book is for Applied Unsupervised Learning with R is designed for business professionals who want to learn about methods to understand their data better, and developers who have an interest in unsupervised learning. Although the book is for beginners, it will be beneficial to have some basic, beginner-level familiarity with R. This includes an understanding of how to open the R console, how to read data, and how to create a loop. To easily understand the concepts of this book, you should also know basic mathematical concepts, including exponents, square roots, means, and medians.

R in Action

Author : Robert I. Kabacoff
Publisher : Simon and Schuster
ISBN 13 : 1638353336
Total Pages : 970 pages
Book Rating : 4.6/5 (383 download)

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Book Synopsis R in Action by : Robert I. Kabacoff

Download or read book R in Action written by Robert I. Kabacoff and published by Simon and Schuster. This book was released on 2015-05-20 with total page 970 pages. Available in PDF, EPUB and Kindle. Book excerpt: Summary R in Action, Second Edition presents both the R language and the examples that make it so useful for business developers. Focusing on practical solutions, the book offers a crash course in statistics and covers elegant methods for dealing with messy and incomplete data that are difficult to analyze using traditional methods. You'll also master R's extensive graphical capabilities for exploring and presenting data visually. And this expanded second edition includes new chapters on time series analysis, cluster analysis, and classification methodologies, including decision trees, random forests, and support vector machines. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Business pros and researchers thrive on data, and R speaks the language of data analysis. R is a powerful programming language for statistical computing. Unlike general-purpose tools, R provides thousands of modules for solving just about any data-crunching or presentation challenge you're likely to face. R runs on all important platforms and is used by thousands of major corporations and institutions worldwide. About the Book R in Action, Second Edition teaches you how to use the R language by presenting examples relevant to scientific, technical, and business developers. Focusing on practical solutions, the book offers a crash course in statistics, including elegant methods for dealing with messy and incomplete data. You'll also master R's extensive graphical capabilities for exploring and presenting data visually. And this expanded second edition includes new chapters on forecasting, data mining, and dynamic report writing. What's Inside Complete R language tutorial Using R to manage, analyze, and visualize data Techniques for debugging programs and creating packages OOP in R Over 160 graphs About the Author Dr. Rob Kabacoff is a seasoned researcher and teacher who specializes in data analysis. He also maintains the popular Quick-R website at statmethods.net. Table of Contents PART 1 GETTING STARTED Introduction to R Creating a dataset Getting started with graphs Basic data management Advanced data management PART 2 BASIC METHODS Basic graphs Basic statistics PART 3 INTERMEDIATE METHODS Regression Analysis of variance Power analysis Intermediate graphs Resampling statistics and bootstrapping PART 4 ADVANCED METHODS Generalized linear models Principal components and factor analysis Time series Cluster analysis Classification Advanced methods for missing data PART 5 EXPANDING YOUR SKILLS Advanced graphics with ggplot2 Advanced programming Creating a package Creating dynamic reports Advanced graphics with the lattice package available online only from manning.com/kabacoff2

Cluster Analysis

Author : Brian S. Everitt
Publisher : John Wiley & Sons
ISBN 13 : 0470978449
Total Pages : 302 pages
Book Rating : 4.4/5 (79 download)

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Book Synopsis Cluster Analysis by : Brian S. Everitt

Download or read book Cluster Analysis written by Brian S. Everitt and published by John Wiley & Sons. This book was released on 2011-01-14 with total page 302 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cluster analysis comprises a range of methods for classifying multivariate data into subgroups. By organizing multivariate data into such subgroups, clustering can help reveal the characteristics of any structure or patterns present. These techniques have proven useful in a wide range of areas such as medicine, psychology, market research and bioinformatics. This fifth edition of the highly successful Cluster Analysis includes coverage of the latest developments in the field and a new chapter dealing with finite mixture models for structured data. Real life examples are used throughout to demonstrate the application of the theory, and figures are used extensively to illustrate graphical techniques. The book is comprehensive yet relatively non-mathematical, focusing on the practical aspects of cluster analysis. Key Features: Presents a comprehensive guide to clustering techniques, with focus on the practical aspects of cluster analysis Provides a thorough revision of the fourth edition, including new developments in clustering longitudinal data and examples from bioinformatics and gene studies./li> Updates the chapter on mixture models to include recent developments and presents a new chapter on mixture modeling for structured data Practitioners and researchers working in cluster analysis and data analysis will benefit from this book.

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

Author : Guojun Gan
Publisher : SIAM
ISBN 13 : 1611976332
Total Pages : 430 pages
Book Rating : 4.6/5 (119 download)

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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 and published by SIAM. This book was released on 2020-11-10 with total page 430 pages. 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.

Cluster Analysis

Author : Brian S. Everitt
Publisher :
ISBN 13 :
Total Pages : 122 pages
Book Rating : 4.:/5 (878 download)

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Book Synopsis Cluster Analysis by : Brian S. Everitt

Download or read book Cluster Analysis written by Brian S. Everitt and published by . This book was released on 1977 with total page 122 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Introduction to Data Science

Author : Rafael A. Irizarry
Publisher : CRC Press
ISBN 13 : 1000708039
Total Pages : 794 pages
Book Rating : 4.0/5 (7 download)

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Book Synopsis Introduction to Data Science by : Rafael A. Irizarry

Download or read book Introduction to Data Science written by Rafael A. Irizarry and published by CRC Press. This book was released on 2019-11-20 with total page 794 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist’s experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state, self-reported student heights, trends in world health and economics, the impact of vaccines on infectious disease rates, the financial crisis of 2007-2008, election forecasting, building a baseball team, image processing of hand-written digits, and movie recommendation systems. The statistical concepts used to answer the case study questions are only briefly introduced, so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises, you will be prepared to learn the more advanced concepts and skills needed to become an expert.

An Introduction to Statistical Learning

Author : Gareth James
Publisher : Springer Nature
ISBN 13 : 3031387473
Total Pages : 617 pages
Book Rating : 4.0/5 (313 download)

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Book Synopsis An Introduction to Statistical Learning by : Gareth James

Download or read book An Introduction to Statistical Learning written by Gareth James and published by Springer Nature. This book was released on 2023-08-01 with total page 617 pages. Available in PDF, EPUB and Kindle. Book excerpt: An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance, marketing, and astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R (ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.

Hands-On Machine Learning with R

Author : Brad Boehmke
Publisher : CRC Press
ISBN 13 : 1000730433
Total Pages : 374 pages
Book Rating : 4.0/5 (7 download)

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Book Synopsis Hands-On Machine Learning with R by : Brad Boehmke

Download or read book Hands-On Machine Learning with R written by Brad Boehmke and published by CRC Press. This book was released on 2019-11-07 with total page 374 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hands-on Machine Learning with R provides a practical and applied approach to learning and developing intuition into today’s most popular machine learning methods. This book serves as a practitioner’s guide to the machine learning process and is meant to help the reader learn to apply the machine learning stack within R, which includes using various R packages such as glmnet, h2o, ranger, xgboost, keras, and others to effectively model and gain insight from their data. The book favors a hands-on approach, providing an intuitive understanding of machine learning concepts through concrete examples and just a little bit of theory. Throughout this book, the reader will be exposed to the entire machine learning process including feature engineering, resampling, hyperparameter tuning, model evaluation, and interpretation. The reader will be exposed to powerful algorithms such as regularized regression, random forests, gradient boosting machines, deep learning, generalized low rank models, and more! By favoring a hands-on approach and using real word data, the reader will gain an intuitive understanding of the architectures and engines that drive these algorithms and packages, understand when and how to tune the various hyperparameters, and be able to interpret model results. By the end of this book, the reader should have a firm grasp of R’s machine learning stack and be able to implement a systematic approach for producing high quality modeling results. Features: · Offers a practical and applied introduction to the most popular machine learning methods. · Topics covered include feature engineering, resampling, deep learning and more. · Uses a hands-on approach and real world data.

Cluster Analysis for Applications

Author : Michael R. Anderberg
Publisher : Academic Press
ISBN 13 : 1483191397
Total Pages : 376 pages
Book Rating : 4.4/5 (831 download)

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Book Synopsis Cluster Analysis for Applications by : Michael R. Anderberg

Download or read book Cluster Analysis for Applications written by Michael R. Anderberg and published by Academic Press. This book was released on 2014-05-10 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cluster Analysis for Applications deals with methods and various applications of cluster analysis. Topics covered range from variables and scales to measures of association among variables and among data units. Conceptual problems in cluster analysis are discussed, along with hierarchical and non-hierarchical clustering methods. The necessary elements of data analysis, statistics, cluster analysis, and computer implementation are integrated vertically to cover the complete path from raw data to a finished analysis. Comprised of 10 chapters, this book begins with an introduction to the subject of cluster analysis and its uses as well as category sorting problems and the need for cluster analysis algorithms. The next three chapters give a detailed account of variables and association measures, with emphasis on strategies for dealing with problems containing variables of mixed types. Subsequent chapters focus on the central techniques of cluster analysis with particular reference to computational considerations; interpretation of clustering results; and techniques and strategies for making the most effective use of cluster analysis. The final chapter suggests an approach for the evaluation of alternative clustering methods. The presentation is capped with a complete set of implementing computer programs listed in the Appendices to make the use of cluster analysis as painless and free of mechanical error as is possible. This monograph is intended for students and workers who have encountered the notion of cluster analysis.

Introduction to Information Retrieval

Author : Christopher D. Manning
Publisher : Cambridge University Press
ISBN 13 : 1139472100
Total Pages : pages
Book Rating : 4.1/5 (394 download)

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Book Synopsis Introduction to Information Retrieval by : Christopher D. Manning

Download or read book Introduction to Information Retrieval written by Christopher D. Manning and published by Cambridge University Press. This book was released on 2008-07-07 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Class-tested and coherent, this textbook teaches classical and web information retrieval, including web search and the related areas of text classification and text clustering from basic concepts. It gives an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections. All the important ideas are explained using examples and figures, making it perfect for introductory courses in information retrieval for advanced undergraduates and graduate students in computer science. Based on feedback from extensive classroom experience, the book has been carefully structured in order to make teaching more natural and effective. Slides and additional exercises (with solutions for lecturers) are also available through the book's supporting website to help course instructors prepare their lectures.

Time Series Clustering and Classification

Author : Elizabeth Ann Maharaj
Publisher : CRC Press
ISBN 13 : 0429603304
Total Pages : 213 pages
Book Rating : 4.4/5 (296 download)

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Book Synopsis Time Series Clustering and Classification by : Elizabeth Ann Maharaj

Download or read book Time Series Clustering and Classification written by Elizabeth Ann Maharaj and published by CRC Press. This book was released on 2019-03-19 with total page 213 pages. Available in PDF, EPUB and Kindle. Book excerpt: The beginning of the age of artificial intelligence and machine learning has created new challenges and opportunities for data analysts, statisticians, mathematicians, econometricians, computer scientists and many others. At the root of these techniques are algorithms and methods for clustering and classifying different types of large datasets, including time series data. Time Series Clustering and Classification includes relevant developments on observation-based, feature-based and model-based traditional and fuzzy clustering methods, feature-based and model-based classification methods, and machine learning methods. It presents a broad and self-contained overview of techniques for both researchers and students. Features Provides an overview of the methods and applications of pattern recognition of time series Covers a wide range of techniques, including unsupervised and supervised approaches Includes a range of real examples from medicine, finance, environmental science, and more R and MATLAB code, and relevant data sets are available on a supplementary website

Practical Guide To Principal Component Methods in R

Author : Alboukadel KASSAMBARA
Publisher : STHDA
ISBN 13 : 1975721136
Total Pages : 169 pages
Book Rating : 4.9/5 (757 download)

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Book Synopsis Practical Guide To Principal Component Methods in R by : Alboukadel KASSAMBARA

Download or read book Practical Guide To Principal Component Methods in R written by Alboukadel KASSAMBARA and published by STHDA. This book was released on 2017-08-23 with total page 169 pages. Available in PDF, EPUB and Kindle. Book excerpt: Although there are several good books on principal component methods (PCMs) and related topics, we felt that many of them are either too theoretical or too advanced. This book provides a solid practical guidance to summarize, visualize and interpret the most important information in a large multivariate data sets, using principal component methods in R. The visualization is based on the factoextra R package that we developed for creating easily beautiful ggplot2-based graphs from the output of PCMs. This book contains 4 parts. Part I provides a quick introduction to R and presents the key features of FactoMineR and factoextra. Part II describes classical principal component methods to analyze data sets containing, predominantly, either continuous or categorical variables. These methods include: Principal Component Analysis (PCA, for continuous variables), simple correspondence analysis (CA, for large contingency tables formed by two categorical variables) and Multiple CA (MCA, for a data set with more than 2 categorical variables). In Part III, you'll learn advanced methods for analyzing a data set containing a mix of variables (continuous and categorical) structured or not into groups: Factor Analysis of Mixed Data (FAMD) and Multiple Factor Analysis (MFA). Part IV covers hierarchical clustering on principal components (HCPC), which is useful for performing clustering with a data set containing only categorical variables or with a mixed data of categorical and continuous variables.

R and Data Mining

Author : Yanchang Zhao
Publisher : Academic Press
ISBN 13 : 012397271X
Total Pages : 256 pages
Book Rating : 4.1/5 (239 download)

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Book Synopsis R and Data Mining by : Yanchang Zhao

Download or read book R and Data Mining written by Yanchang Zhao and published by Academic Press. This book was released on 2012-12-31 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: R and Data Mining introduces researchers, post-graduate students, and analysts to data mining using R, a free software environment for statistical computing and graphics. The book provides practical methods for using R in applications from academia to industry to extract knowledge from vast amounts of data. Readers will find this book a valuable guide to the use of R in tasks such as classification and prediction, clustering, outlier detection, association rules, sequence analysis, text mining, social network analysis, sentiment analysis, and more. Data mining techniques are growing in popularity in a broad range of areas, from banking to insurance, retail, telecom, medicine, research, and government. This book focuses on the modeling phase of the data mining process, also addressing data exploration and model evaluation. With three in-depth case studies, a quick reference guide, bibliography, and links to a wealth of online resources, R and Data Mining is a valuable, practical guide to a powerful method of analysis. Presents an introduction into using R for data mining applications, covering most popular data mining techniques Provides code examples and data so that readers can easily learn the techniques Features case studies in real-world applications to help readers apply the techniques in their work