Clustering And Classification
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Clustering and Classification
Author | : Phipps Arabie,Geert de Soete |
Publsiher | : World Scientific |
Total Pages | : 508 |
Release | : 1996 |
Genre | : Mathematics |
ISBN | : 9810212879 |
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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.
Classification Clustering and Data Analysis
Author | : Krzystof Jajuga,Andrzej Sokolowski,Hans-Hermann Bock |
Publsiher | : Springer Science & Business Media |
Total Pages | : 468 |
Release | : 2012-12-06 |
Genre | : Computers |
ISBN | : 9783642561818 |
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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.
Model Based Clustering and Classification for Data Science
Author | : Charles Bouveyron,Gilles Celeux,T. Brendan Murphy,Adrian E. Raftery |
Publsiher | : Cambridge University Press |
Total Pages | : 446 |
Release | : 2019-07-25 |
Genre | : Business & Economics |
ISBN | : 9781108494205 |
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Colorful example-rich introduction to the state-of-the-art for students in data science, as well as researchers and practitioners.
Time Series Clustering and Classification
Author | : Elizabeth Ann Maharaj,Pierpaolo D'Urso,Jorge Caiado |
Publsiher | : CRC Press |
Total Pages | : 213 |
Release | : 2019-03-19 |
Genre | : Mathematics |
ISBN | : 9780429603303 |
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The beginning of the age of artificial intelligence and machine learning has created new challenges and opportunities for data analysts, statisticians, mathematicians, econometricians, computer scientists and many others. At the root of these techniques are algorithms and methods for clustering and classifying different types of large datasets, including time series data. Time Series Clustering and Classification includes relevant developments on observation-based, feature-based and model-based traditional and fuzzy clustering methods, feature-based and model-based classification methods, and machine learning methods. It presents a broad and self-contained overview of techniques for both researchers and students. Features Provides an overview of the methods and applications of pattern recognition of time series Covers a wide range of techniques, including unsupervised and supervised approaches Includes a range of real examples from medicine, finance, environmental science, and more R and MATLAB code, and relevant data sets are available on a supplementary website
Mathematical Classification and Clustering
Author | : Boris Mirkin |
Publsiher | : Springer Science & Business Media |
Total Pages | : 439 |
Release | : 2013-12-01 |
Genre | : Mathematics |
ISBN | : 9781461304579 |
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I am very happy to have this opportunity to present the work of Boris Mirkin, a distinguished Russian scholar in the areas of data analysis and decision making methodologies. The monograph is devoted entirely to clustering, a discipline dispersed through many theoretical and application areas, from mathematical statistics and combina torial optimization to biology, sociology and organizational structures. It compiles an immense amount of research done to date, including many original Russian de velopments never presented to the international community before (for instance, cluster-by-cluster versions of the K-Means method in Chapter 4 or uniform par titioning in Chapter 5). The author's approach, approximation clustering, allows him both to systematize a great part of the discipline and to develop many in novative methods in the framework of optimization problems. The optimization methods considered are proved to be meaningful in the contexts of data analysis and clustering. The material presented in this book is quite interesting and stimulating in paradigms, clustering and optimization. On the other hand, it has a substantial application appeal. The book will be useful both to specialists and students in the fields of data analysis and clustering as well as in biology, psychology, economics, marketing research, artificial intelligence, and other scientific disciplines. Panos Pardalos, Series Editor.
Classification Clustering and Data Mining Applications
Author | : International Federation of Classification Societies. Conference |
Publsiher | : Springer Science & Business Media |
Total Pages | : 676 |
Release | : 2004-06-09 |
Genre | : Computers |
ISBN | : 9783540220145 |
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Modern data analysis stands at the interface of statistics, computer science, and discrete mathematics. This volume describes new methods in this area, with special emphasis on classification and cluster analysis. Those methods are applied to problems in information retrieval, phylogeny, medical diagnosis, microarrays, and other active research areas.
Text Mining
Author | : Ashok N. Srivastava,Mehran Sahami |
Publsiher | : CRC Press |
Total Pages | : 330 |
Release | : 2009-06-15 |
Genre | : Business & Economics |
ISBN | : 9781420059458 |
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The Definitive Resource on Text Mining Theory and Applications from Foremost Researchers in the FieldGiving a broad perspective of the field from numerous vantage points, Text Mining: Classification, Clustering, and Applications focuses on statistical methods for text mining and analysis. It examines methods to automatically cluster and classify te
Classification Clustering and Data Mining Applications
Author | : David Banks,Leanna House,Frederick R. McMorris,Phipps Arabie,Wolfgang A. Gaul |
Publsiher | : Springer Science & Business Media |
Total Pages | : 642 |
Release | : 2011-01-07 |
Genre | : Language Arts & Disciplines |
ISBN | : 9783642171031 |
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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.