Model-Based Clustering and Classification for Data Science - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Model-Based Clustering and Classification for Data Science write by Charles Bouveyron. This book was released on 2019-07-25. Model-Based Clustering and Classification for Data Science available in PDF, EPUB and Kindle. 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.
Model-Based Clustering and Classification for Data Science
Model-Based Clustering and Classification for Data Science - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Model-Based Clustering and Classification for Data Science write by Charles Bouveyron. This book was released on 2019-07-25. Model-Based Clustering and Classification for Data Science available in PDF, EPUB and Kindle. 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
Time Series Clustering and Classification - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Time Series Clustering and Classification write by Elizabeth Ann Maharaj. This book was released on 2019-03-19. Time Series Clustering and Classification available in PDF, EPUB and Kindle. 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
Classification, Clustering, and Data Analysis
Classification, Clustering, and Data Analysis - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Classification, Clustering, and Data Analysis write by Krzystof Jajuga. This book was released on 2012-12-06. Classification, Clustering, and Data Analysis available in PDF, EPUB and Kindle. 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.
Data Clustering
Data Clustering - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Data Clustering write by Charu C. Aggarwal. This book was released on 2013-08-21. Data Clustering available in PDF, EPUB and Kindle. Research on the problem of clustering tends to be fragmented across the pattern recognition, database, data mining, and machine learning communities. Addressing this problem in a unified way, Data Clustering: Algorithms and Applications provides complete coverage of the entire area of clustering, from basic methods to more refined and complex data clustering approaches. It pays special attention to recent issues in graphs, social networks, and other domains. The book focuses on three primary aspects of data clustering: Methods, describing key techniques commonly used for clustering, such as feature selection, agglomerative clustering, partitional clustering, density-based clustering, probabilistic clustering, grid-based clustering, spectral clustering, and nonnegative matrix factorization Domains, covering methods used for different domains of data, such as categorical data, text data, multimedia data, graph data, biological data, stream data, uncertain data, time series clustering, high-dimensional clustering, and big data Variations and Insights, discussing important variations of the clustering process, such as semisupervised clustering, interactive clustering, multiview clustering, cluster ensembles, and cluster validation In this book, top researchers from around the world explore the characteristics of clustering problems in a variety of application areas. They also explain how to glean detailed insight from the clustering process—including how to verify the quality of the underlying clusters—through supervision, human intervention, or the automated generation of alternative clusters.
