Introduction to Clustering Large and High-Dimensional Data

Introduction to Clustering Large and High-Dimensional Data
Title Introduction to Clustering Large and High-Dimensional Data PDF eBook
Author Jacob Kogan
Publisher Cambridge University Press
Pages 228
Release 2007
Genre Computers
ISBN 9780521617932

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Focuses on a few of the important clustering algorithms in the context of information retrieval.

New Directions in Statistical Physics

New Directions in Statistical Physics
Title New Directions in Statistical Physics PDF eBook
Author Luc T. Wille
Publisher Springer Science & Business Media
Pages 369
Release 2013-03-09
Genre Science
ISBN 3662089688

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This book provides a unique insight into the latest breakthroughs in a consistent manner, at a level accessible to undergraduates, yet with enough attention to the theory and computation to satisfy the professional researcher Statistical physics addresses the study and understanding of systems with many degrees of freedom. As such it has a rich and varied history, with applications to thermodynamics, magnetic phase transitions, and order/disorder transformations, to name just a few. However, the tools of statistical physics can be profitably used to investigate any system with a large number of components. Thus, recent years have seen these methods applied in many unexpected directions, three of which are the main focus of this volume. These applications have been remarkably successful and have enriched the financial, biological, and engineering literature. Although reported in the physics literature, the results tend to be scattered and the underlying unity of the field overlooked.

Clustering High--Dimensional Data

Clustering High--Dimensional Data
Title Clustering High--Dimensional Data PDF eBook
Author Francesco Masulli
Publisher Springer
Pages 157
Release 2015-11-24
Genre Computers
ISBN 366248577X

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This book constitutes the proceedings of the International Workshop on Clustering High-Dimensional Data, CHDD 2012, held in Naples, Italy, in May 2012. The 9 papers presented in this volume were carefully reviewed and selected from 15 submissions. They deal with the general subject and issues of high-dimensional data clustering; present examples of techniques used to find and investigate clusters in high dimensionality; and the most common approach to tackle dimensionality problems, namely, dimensionality reduction and its application in clustering.

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

Data Clustering: Theory, Algorithms, and Applications, Second Edition
Title Data Clustering: Theory, Algorithms, and Applications, Second Edition PDF eBook
Author Guojun Gan
Publisher SIAM
Pages 430
Release 2020-11-10
Genre Mathematics
ISBN 1611976332

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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 for Applications

Cluster Analysis for Applications
Title Cluster Analysis for Applications PDF eBook
Author Michael R. Anderberg
Publisher Academic Press
Pages 376
Release 2014-05-10
Genre Mathematics
ISBN 1483191397

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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.

Grouping Multidimensional Data

Grouping Multidimensional Data
Title Grouping Multidimensional Data PDF eBook
Author Jacob Kogan
Publisher Taylor & Francis
Pages 296
Release 2006-02-10
Genre Computers
ISBN 9783540283485

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Finite Mixture and Markov Switching Models

Finite Mixture and Markov Switching Models
Title Finite Mixture and Markov Switching Models PDF eBook
Author Sylvia Frühwirth-Schnatter
Publisher Springer Science & Business Media
Pages 506
Release 2006-11-24
Genre Mathematics
ISBN 0387357688

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The past decade has seen powerful new computational tools for modeling which combine a Bayesian approach with recent Monte simulation techniques based on Markov chains. This book is the first to offer a systematic presentation of the Bayesian perspective of finite mixture modelling. The book is designed to show finite mixture and Markov switching models are formulated, what structures they imply on the data, their potential uses, and how they are estimated. Presenting its concepts informally without sacrificing mathematical correctness, it will serve a wide readership including statisticians as well as biologists, economists, engineers, financial and market researchers.