Classification, Clustering, and Data Analysis

Classification, Clustering, and Data Analysis
Author: Krzystof Jajuga
Publisher: Springer Science & Business Media
Total Pages: 468
Release: 2012-12-06
Genre: 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.

Classification, Clustering, and Data Mining Applications

Classification, Clustering, and Data Mining Applications
Author: David Banks
Publisher: Springer Science & Business Media
Total Pages: 642
Release: 2011-01-07
Genre: 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.

Model-Based Clustering and Classification for Data Science

Model-Based Clustering and Classification for Data Science
Author: Charles Bouveyron
Publisher: Cambridge University Press
Total Pages: 447
Release: 2019-07-25
Genre: 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.

Fuzzy Cluster Analysis

Fuzzy Cluster Analysis
Author: Frank Höppner
Publisher: John Wiley & Sons
Total Pages: 308
Release: 1999-07-09
Genre: 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)

Data Science

Data Science
Author: Francesco Palumbo
Publisher: Springer
Total Pages: 346
Release: 2017-07-04
Genre: 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.

Clustering and Classification

Clustering and Classification
Author: Phipps Arabie
Publisher: World Scientific
Total Pages: 508
Release: 1996
Genre: 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.

Selected Contributions in Data Analysis and Classification

Selected Contributions in Data Analysis and Classification
Author: Paula Brito
Publisher: Springer Science & Business Media
Total Pages: 619
Release: 2007-08-16
Genre: 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.

Classification, Data Analysis, and Knowledge Organization

Classification, Data Analysis, and Knowledge Organization
Author: Hans-Hermann Bock
Publisher: Springer Science & Business Media
Total Pages: 404
Release: 2012-12-06
Genre: Business & Economics
ISBN: 3642763073

In science, industry, public administration and documentation centers large amounts of data and information are collected which must be analyzed, ordered, visualized, classified and stored efficiently in order to be useful for practical applications. This volume contains 50 selected theoretical and applied papers presenting a wealth of new and innovative ideas, methods, models and systems which can be used for this purpose. It combines papers and strategies from two main streams of research in an interdisciplinary, dynamic and exciting way: On the one hand, mathematical and statistical methods are described which allow a quantitative analysis of data, provide strategies for classifying objects or making exploratory searches for interesting structures, and give ways to make comprehensive graphical displays of large arrays of data. On the other hand, papers related to information sciences, informatics and data bank systems provide powerful tools for representing, modelling, storing and retrieving facts, data and knowledge characterized by qualitative descriptors, semantic relations, or linguistic concepts. The integration of both fields and a special part on applied problems from biology, medicine, archeology, industry and administration assure that this volume will be informative and useful for theory and practice.