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Clustering and Classification

1996 Mathematics
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Release : 1996
Genre : Mathematics
Kind : eBook
Book Rating : 872/5 ( reviews)

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Book Synopsis Clustering and Classification by : Phipps Arabie

Download or read book Clustering and Classification written by Phipps Arabie. This book was released on 1996. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Model-Based Clustering and Classification for Data Science

2019-07-25 Mathematics
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Release : 2019-07-25
Genre : Mathematics
Kind : eBook
Book Rating : 591/5 ( reviews)

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Book Synopsis Model-Based Clustering and Classification for Data Science by : Charles Bouveyron

Download or read book Model-Based Clustering and Classification for Data Science written by Charles Bouveyron. This book was released on 2019-07-25. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Classification, Clustering, and Data Analysis

2012-12-06 Computers
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Release : 2012-12-06
Genre : Computers
Kind : eBook
Book Rating : 810/5 ( reviews)

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Book Synopsis Classification, Clustering, and Data Analysis by : Krzystof Jajuga

Download or read book Classification, Clustering, and Data Analysis written by Krzystof Jajuga. This book was released on 2012-12-06. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Time Series Clustering and Classification

2019-03-19 Mathematics
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Release : 2019-03-19
Genre : Mathematics
Kind : eBook
Book Rating : 304/5 ( reviews)

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Book Synopsis Time Series Clustering and Classification by : Elizabeth Ann Maharaj

Download or read book Time Series Clustering and Classification written by Elizabeth Ann Maharaj. This book was released on 2019-03-19. Available in PDF, EPUB and Kindle. Book excerpt: 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

2013-12-01 Mathematics
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Release : 2013-12-01
Genre : Mathematics
Kind : eBook
Book Rating : 571/5 ( reviews)

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Book Synopsis Mathematical Classification and Clustering by : Boris Mirkin

Download or read book Mathematical Classification and Clustering written by Boris Mirkin. This book was released on 2013-12-01. Available in PDF, EPUB and Kindle. Book excerpt: 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.

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