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Author: Rui Xu
Publisher: John Wiley & Sons
ISBN: 0470382783
Category : Mathematics
Languages : en
Pages : 400
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Book Description
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.
Author: Rui Xu
Publisher: John Wiley & Sons
ISBN: 0470382783
Category : Mathematics
Languages : en
Pages : 400
Get Book Here
Book Description
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.
Author: Ronald S. King
Publisher: Mercury Learning and Information
ISBN: 1942270135
Category : Computers
Languages : en
Pages : 363
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Book Description
Cluster analysis is used in data mining and is a common technique for statistical data analysis used in many fields of study, such as the medical & life sciences, behavioral & social sciences, engineering, and in computer science. Designed for training industry professionals or for a course on clustering and classification, it can also be used as a companion text for applied statistics. No previous experience in clustering or data mining is assumed. Informal algorithms for clustering data and interpreting results are emphasized. In order to evaluate the results of clustering and to explore data, graphical methods and data structures are used for representing data. Throughout the text, examples and references are provided, in order to enable the material to be comprehensible for a diverse audience. A companion disc includes numerous appendices with programs, data, charts, solutions, etc. eBook Customers: Companion files are available for downloading with order number/proof of purchase by writing to the publisher at
[email protected]. FEATURES *Places emphasis on illustrating the underlying logic in making decisions during the cluster analysis *Discusses the related applications of statistic, e.g., Ward’s method (ANOVA), JAN (regression analysis & correlational analysis), cluster validation (hypothesis testing, goodness-of-fit, Monte Carlo simulation, etc.) *Contains separate chapters on JAN and the clustering of categorical data *Includes a companion disc with solutions to exercises, programs, data sets, charts, etc.
Author: Richard Harris
Publisher: John Wiley & Sons
ISBN: 047086415X
Category : Science
Languages : en
Pages : 328
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Book Description
Geodemographic classification is ‘big business’ in the marketing and service sector industries, and in public policy there has also been a resurgence of interest in neighbourhood initiatives and targeting. As an increasing number of professionals realise the potential of geographic analysis for their business or organisation, there exists a timely gap in the market for a focussed book on geodemographics and GIS. Geodemographics: neighbourhood targeting and GIS provides both an introduction to and overview of the methods, theory and classification techniques that provide the foundation of neighbourhood analysis and commercial geodemographic products. Particular focus is given to the presentation and use of neighbourhood classification in GIS. Authored by leading marketing professionals and a prominent academic, this book presents methods, theory and classification techniques in a reader-friendly manner Supported by private and public sector case studies and vignettes The applied ‘how to’ sections will specifically appeal to the intended audience at work in business and service planning Includes information on the recent UK and US Census products and resulting neighbourhood classifications
Author: Trevor Bihl
Publisher: SAS Institute
ISBN: 1635262410
Category : Computers
Languages : en
Pages : 472
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Book Description
Analyze your biostatistics data with JMP! Trevor Bihl's Biostatistics Using JMP: A Practical Guide provides a practical introduction on using JMP, the interactive statistical discovery software, to solve biostatistical problems. Providing extensive breadth, from summary statistics to neural networks, this essential volume offers a comprehensive, step-by-step guide to using JMP to handle your data. The first biostatistical book to focus on software, Biostatistics Using JMP discusses such topics as data visualization, data wrangling, data cleaning, histograms, box plots, Pareto plots, scatter plots, hypothesis tests, confidence intervals, analysis of variance, regression, curve fitting, clustering, classification, discriminant analysis, neural networks, decision trees, logistic regression, survival analysis, control charts, and metaanalysis. Written for university students, professors, those who perform biological/biomedical experiments, laboratory managers, and research scientists, Biostatistics Using JMP provides a practical approach to using JMP to solve your biostatistical problems.
Author: P.J. Reynolds
Publisher: Elsevier
ISBN: 1483297527
Category : Science
Languages : en
Pages : 422
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Book Description
This book attempts to answer why there is so much interest in clusters. Clusters occur on all length scales, and as a result occur in a variety of fields. Clusters are interesting scientifically, but they also have important consequences technologically. The division of the book into three parts roughly separates the field into small, intermediate, and large-scale clusters. Small clusters are the regime of atomic and molecular physics and chemistry. The intermediate regime is the transitional regime, with its characteristics including the onset of bulk-like behavior, growth and aggregation, and the beginning of materials properties. Large-scale clusters reflect more condensed-matter and materials science aspects and it is in this regime that fractals make their most dramatic appearance. This well-integrated and pedagogical overview of the wide field of clusters in which both theoretical and experimental work is covered, will be of interest not only to students, advanced undergraduates and graduate students, but also to researchers in the various subfields surveyed.
Author: John A. Hartigan
Publisher: John Wiley & Sons
ISBN:
Category : Mathematics
Languages : en
Pages : 374
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Book Description
Shows how Galileo, Newton, and Einstein tried to explain gravity. Discusses the concept of microgravity and NASA's research on gravity and microgravity.
Author: Brian S. Everitt
Publisher: John Wiley & Sons
ISBN: 0470978449
Category : Mathematics
Languages : en
Pages : 302
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Book Description
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.
Author: Paolo Giordani
Publisher: Springer Nature
ISBN: 9811305536
Category : Mathematics
Languages : en
Pages : 346
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Book Description
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.
Author: Michael R. Anderberg
Publisher: Academic Press
ISBN: 1483191397
Category : Mathematics
Languages : en
Pages : 376
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Book Description
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.
Author: Charles Bouveyron
Publisher: Cambridge University Press
ISBN: 1108640591
Category : Mathematics
Languages : en
Pages : 447
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Book Description
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.
