Cluster Analysis
Download Cluster Analysis PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Cluster Analysis book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Cluster Analysis and Applications
With the development of Big Data platforms for managing massive amount of data and wide availability of tools for processing these data, the biggest limitation is the lack of trained experts who are qualified to process and interpret the results. This textbook is intended for graduate students and experts using methods of cluster analysis and applications in various fields. Suitable for an introductory course on cluster analysis or data mining, with an in-depth mathematical treatment that includes discussions on different measures, primitives (points, lines, etc.) and optimization-based clustering methods, Cluster Analysis and Applications also includes coverage of deep learning based clustering methods. With clear explanations of ideas and precise definitions of concepts, accompanied by numerous examples and exercises together with Mathematica programs and modules, Cluster Analysis and Applications may be used by students and researchers in various disciplines, working in data analysis or data science.
Cluster Analysis for Data Mining and System Identification
Author: János Abonyi
language: en
Publisher: Springer Science & Business Media
Release Date: 2007-06-22
The aim of this book is to illustrate that advanced fuzzy clustering algorithms can be used not only for partitioning of the data. It can also be used for visualization, regression, classification and time-series analysis, hence fuzzy cluster analysis is a good approach to solve complex data mining and system identification problems. This book is oriented to undergraduate and postgraduate and is well suited for teaching purposes.
Cluster Analysis
Cluster analysis comprises a range of methods of classifying multivariate data into subgroups and these techniques are widely applicable. This new edition incorporates material covering developing areas such as Bayesian statistics & neural networks.