Cluster Analysis

1 Introduction2 Representatives2.1 Representative of data sets with one feature2.1.1 Best LS-representativ2.1.2 Best 1-representative2.1.3 Best representative of weighted data2.1.4 Bregman divergences2.2 Representative of data sets with two features2.2.1 Fermat-Torricelli-Weber problem2.2.2 Centroid of a set in the plane2.2.3 Median of a set in the plane 2.2.4 Geometric median of a set in the plane2.3 Representative of data sets with several features2.3.1 Representative of weighted data2.4 Representative of periodic data2.4.1 Representative of data on the unit circle2.4.2 Burn diagram3 Data clustering3.1 Optimal k-partition3.1.1 Minimal distance principle and Voronoi diagram3.1.2 k-means algorithm3.2 Clustering data with one feature3.2.1 Application of the LS-distance-like function3.2.2 The dual problem3.2.3 Least absolute deviation principle3.2.4 Clustering weighted data3.3 Clustering data with two or several features3.3.1 Least squares principle3.3.2 The dual problem3.3.3 Least absol

This book provides the reader with a basic understanding of the formal concepts of the cluster, clustering, partition, cluster analysis etc. The book explains feature-based, graph-based and spectral c

This book is published open access under a CC BY 4.0 license.It covers aspects of unsupervised machine learning used for knowledge discovery in data science and introduces a data-driven approach to cl

Clustering techniques are increasingly being put to use in the analysis of high-throughput biological datasets. Novel computational techniques to analyse high throughput data in the form of sequences,

In order to show how linguists can use cluster analysis--which is used to generate hypotheses in a broad range of sciences--for corpus-based hypothesis generation in linguistics, Moisl applies it to a

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,

Clustering remains a vibrant area of research in statistics, and although there are many books on this topic, there are relatively few that are well-founded in the theoretical aspects. This book overv

This collection does not simply focus on a set of methods, but in presenting a range of existing work together, the logic of what is arguably a methodological phase-shift in quantitative research is e

?? ?Cluster analysis is a multivariate classification technique that allows for identification of homogenous subgroups within diverse samples based on shared characteristics. In recent years, cluster

This edition provides a thorough revision of the fourth edition which focuses on the practical aspects of cluster analysis and covers new methodology in terms of longitudinal data and provides exampl

Design of water control structures, reservoir management, economic evaluation of flood protection projects, land use planning and management, flood insurance assessment, and other projects rely on kn

"This book presents new approaches to data mining and system identification. Algorithms that can be used for the clustering of data have been overviewed. New techniques and tools are presented for the

The application of cluster analysis (collecting entities that belong together in some sense in a class) is widespread in engineering, medicine, biology, social science, and many other areas. This hand

The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabr

Provides a timely and important introduction to fuzzy cluster analysis, its methods and areas of application, systematically describing different fuzzy clustering techniques so the user may choose met

Although clustering--the classification of objects into meaningful sets--is an important procedure in the social sciences today, cluster analysis as a multivariate statistical procedure is poorly und

A cluster randomization trial is one in which intact social units, or clusters of individuals, are randomized to different intervention groups. Trials randomizing clusters have become particularly wid

Cluster Randomised Trials discusses the design, conduct, and analysis of health trials that randomise groups of individuals to different treatments. It explores the advantages of cluster randomization

This book describes the importance of the emerging technique Cluster Secondary Ion Mass Spectrometry (SIMS), which is used for the analysis of a range of solid materials, including everything from org

Recent economic transformations in the world economy are progressing in two divergent directions – international production fragmentation and industrial agglomeration. Based on extensive data analysis

Advances in experimental methods have resulted in the generation of enormous volumes of data across the life sciences. Hence clustering and classification techniques that were once predominantly the domain of ecologists are now being used more widely. This 2006 book provides an overview of these important data analysis methods, from long-established statistical methods to more recent machine learning techniques. It aims to provide a framework that will enable the reader to recognise the assumptions and constraints that are implicit in all such techniques. Important generic issues are discussed first and then the major families of algorithms are described. Throughout the focus is on explanation and understanding and readers are directed to other resources that provide additional mathematical rigour when it is required. Examples taken from across the whole of biology, including bioinformatics, are provided throughout the book to illustrate the key concepts and each technique's potential.

The book provides an introduction to the theory of cluster sets, a branch of topological analysis which has made great strides in recent years. The cluster set of a function at a particular point is the set of limit values of the function at that point which may be either a boundary point or (in the case of a non-analytic function) an interior point of the function's domain. In topological analysis, its main application is to problems arising in the theory of functions of a complex variable, with particular reference to boundary behaviour such as the theory of prime ends under conformal mapping. An important and novel feature of the book is the discussion of more general applications to non-analytic functions, including arbitrary functions. The authors assume a general familiarity with classical function theory but include the more specialised material required for the development of the theory of cluster sets, so making the treatment accessible to graduate students.

A much-needed guide to the design and analysis of cluster randomized trials, How to Design, Analyse and Report Cluster Randomised Trials in Medicine and Health Related Research delivers practical guid