Author : Krzystof Jajuga
Publisher : Springer Science & Business Media
Page : 468 pages
File Size : 42,80 MB
Release : 2012-12-06
Category : Computers
ISBN : 3642561810
The book presents a long list of useful methods for classification, clustering and data analysis. By combining theoretical aspects with practical problems, it is designed for researchers as well as for applied statisticians and will support the fast transfer of new methodological advances to a wide range of applications.
Author : David Banks
Publisher : Springer Science & Business Media
Page : 642 pages
File Size : 19,1 MB
Release : 2011-01-07
Category : Language Arts & Disciplines
ISBN : 3642171036
This volume describes new methods with special emphasis on classification and cluster analysis. These methods are applied to problems in information retrieval, phylogeny, medical diagnosis, microarrays, and other active research areas.
Author : Charles Bouveyron
Publisher : Cambridge University Press
Page : 447 pages
File Size : 17,60 MB
Release : 2019-07-25
Category : Mathematics
ISBN : 1108640591
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.
Author : Phipps Arabie
Publisher : World Scientific
Page : 508 pages
File Size : 36,88 MB
Release : 1996
Category : Mathematics
ISBN : 9789810212872
At a moderately advanced level, this book seeks to cover the areas of clustering and related methods of data analysis where major advances are being made. Topics include: hierarchical clustering, variable selection and weighting, additive trees and other network models, relevance of neural network models to clustering, the role of computational complexity in cluster analysis, latent class approaches to cluster analysis, theory and method with applications of a hierarchical classes model in psychology and psychopathology, combinatorial data analysis, clusterwise aggregation of relations, review of the Japanese-language results on clustering, review of the Russian-language results on clustering and multidimensional scaling, practical advances, and significance tests.
Author : Francesco Palumbo
Publisher : Springer
Page : 346 pages
File Size : 10,73 MB
Release : 2017-07-04
Category : Mathematics
ISBN : 3319557238
This edited volume on the latest advances in data science covers a wide range of topics in the context of data analysis and classification. In particular, it includes contributions on classification methods for high-dimensional data, clustering methods, multivariate statistical methods, and various applications. The book gathers a selection of peer-reviewed contributions presented at the Fifteenth Conference of the International Federation of Classification Societies (IFCS2015), which was hosted by the Alma Mater Studiorum, University of Bologna, from July 5 to 8, 2015.
Author : Guojun Gan
Publisher : SIAM
Page : 430 pages
File Size : 38,53 MB
Release : 2020-11-10
Category : Mathematics
ISBN : 1611976332
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.
Author : Krzystof Jajuga
Publisher :
Page : 508 pages
File Size : 41,86 MB
Release : 2002-06-26
Category :
ISBN : 9783642561825
Author : Frank Höppner
Publisher : John Wiley & Sons
Page : 308 pages
File Size : 16,68 MB
Release : 1999-07-09
Category : Science
ISBN : 9780471988649
Dieser Band konzentriert sich auf Konzepte, Algorithmen und Anwendungen des Fuzzy Clustering. In sich geschlossen werden Techniken wie das Fuzzy-c-Mittel und die Gustafson-Kessel- und Gath- und Gava-Algorithmen behandelt, wobei vom Leser keine Vorkenntnisse auf dem Gebiet von Fuzzy-Systemen erwartet werden. Durch anschauliche Anwendungsbeispiele eignet sich das Buch als Einführung für Praktiker der Datenanalyse, der Bilderkennung und der angewandten Mathematik. (05/99)
Author : Ronald S. King
Publisher : Mercury Learning and Information
Page : 363 pages
File Size : 50,72 MB
Release : 2015-05-12
Category : Computers
ISBN : 1942270135
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 : Paula Brito
Publisher : Springer Science & Business Media
Page : 619 pages
File Size : 25,15 MB
Release : 2007-08-16
Category : Mathematics
ISBN : 3540735607
This volume presents recent methodological developments in data analysis and classification. It covers a wide range of topics, including methods for classification and clustering, dissimilarity analysis, consensus methods, conceptual analysis of data, and data mining and knowledge discovery in databases. The book also presents a wide variety of applications, in fields such as biology, micro-array analysis, cyber traffic, and bank fraud detection.
