Mathematics Of Data Science A Computational Approach To Clustering And Classification

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Mathematics of Data Science: A Computational Approach to Clustering and Classification

Daniela Calvetti,Erkki Somersalo
Mathematics of Data Science A Computational Approach to Clustering and Classification Book PDF

Author : Daniela Calvetti,Erkki Somersalo
Publisher : SIAM
Page : 199 pages
File Size : 45,9 Mb
Release : 2020-11-20
Category : Mathematics
ISBN : 9781611976373

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Mathematics of Data Science: A Computational Approach to Clustering and Classification by Daniela Calvetti,Erkki Somersalo Pdf

This textbook provides a solid mathematical basis for understanding popular data science algorithms for clustering and classification and shows that an in-depth understanding of the mathematics powering these algorithms gives insight into the underlying data. It presents a step-by-step derivation of these algorithms, outlining their implementation from scratch in a computationally sound way. Mathematics of Data Science: A Computational Approach to Clustering and Classification proposes different ways of visualizing high-dimensional data to unveil hidden internal structures, and nearly every chapter includes graphical explanations and computed examples using publicly available data sets to highlight similarities and differences among the algorithms. This self-contained book is geared toward advanced undergraduate and beginning graduate students in the mathematical sciences, engineering, and computer science and can be used as the main text in a semester course. Researchers in any application area where data science methods are used will also find the book of interest. No advanced mathematical or statistical background is assumed.

Model-Based Clustering and Classification for Data Science

Charles Bouveyron,Gilles Celeux,T. Brendan Murphy,Adrian E. Raftery
Model Based Clustering and Classification for Data Science Book PDF

Author : Charles Bouveyron,Gilles Celeux,T. Brendan Murphy,Adrian E. Raftery
Publisher : Cambridge University Press
Page : 446 pages
File Size : 53,7 Mb
Release : 2019-07-25
Category : Business & Economics
ISBN : 9781108494205

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Model-Based Clustering and Classification for Data Science by Charles Bouveyron,Gilles Celeux,T. Brendan Murphy,Adrian E. Raftery Pdf

Colorful example-rich introduction to the state-of-the-art for students in data science, as well as researchers and practitioners.

Clustering and Classification

Phipps Arabie,Geert de Soete
Clustering and Classification Book PDF

Author : Phipps Arabie,Geert de Soete
Publisher : World Scientific
Page : 508 pages
File Size : 51,8 Mb
Release : 1996
Category : Mathematics
ISBN : 9810212879

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Clustering and Classification by Phipps Arabie,Geert de Soete Pdf

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.

Mathematical Classification and Clustering

Boris Mirkin
Mathematical Classification and Clustering Book PDF

Author : Boris Mirkin
Publisher : Springer Science & Business Media
Page : 439 pages
File Size : 55,8 Mb
Release : 2013-12-01
Category : Mathematics
ISBN : 9781461304579

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Mathematical Classification and Clustering by Boris Mirkin Pdf

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 Analysis

Krzystof Jajuga,Andrzej Sokolowski,Hans-Hermann Bock
Classification Clustering and Data Analysis Book PDF

Author : Krzystof Jajuga,Andrzej Sokolowski,Hans-Hermann Bock
Publisher : Springer Science & Business Media
Page : 468 pages
File Size : 42,9 Mb
Release : 2012-12-06
Category : Computers
ISBN : 9783642561818

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Classification, Clustering, and Data Analysis by Krzystof Jajuga,Andrzej Sokolowski,Hans-Hermann Bock Pdf

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

International Federation of Classification Societies. Conference
Classification Clustering and Data Mining Applications Book PDF

Author : International Federation of Classification Societies. Conference
Publisher : Springer Science & Business Media
Page : 676 pages
File Size : 48,9 Mb
Release : 2004-06-09
Category : Computers
ISBN : 9783540220145

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Classification, Clustering, and Data Mining Applications by International Federation of Classification Societies. Conference Pdf

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.

Mathematical Problems in Data Science

Li M. Chen,Zhixun Su,Bo Jiang
Mathematical Problems in Data Science Book PDF

Author : Li M. Chen,Zhixun Su,Bo Jiang
Publisher : Springer
Page : 213 pages
File Size : 47,5 Mb
Release : 2015-12-15
Category : Computers
ISBN : 9783319251271

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Mathematical Problems in Data Science by Li M. Chen,Zhixun Su,Bo Jiang Pdf

This book describes current problems in data science and Big Data. Key topics are data classification, Graph Cut, the Laplacian Matrix, Google Page Rank, efficient algorithms, hardness of problems, different types of big data, geometric data structures, topological data processing, and various learning methods. For unsolved problems such as incomplete data relation and reconstruction, the book includes possible solutions and both statistical and computational methods for data analysis. Initial chapters focus on exploring the properties of incomplete data sets and partial-connectedness among data points or data sets. Discussions also cover the completion problem of Netflix matrix; machine learning method on massive data sets; image segmentation and video search. This book introduces software tools for data science and Big Data such MapReduce, Hadoop, and Spark. This book contains three parts. The first part explores the fundamental tools of data science. It includes basic graph theoretical methods, statistical and AI methods for massive data sets. In second part, chapters focus on the procedural treatment of data science problems including machine learning methods, mathematical image and video processing, topological data analysis, and statistical methods. The final section provides case studies on special topics in variational learning, manifold learning, business and financial data rec overy, geometric search, and computing models. Mathematical Problems in Data Science is a valuable resource for researchers and professionals working in data science, information systems and networks. Advanced-level students studying computer science, electrical engineering and mathematics will also find the content helpful.

Data Science and Machine Learning

Dirk P. Kroese,Zdravko Botev,Thomas Taimre,Radislav Vaisman
Data Science and Machine Learning Book PDF

Author : Dirk P. Kroese,Zdravko Botev,Thomas Taimre,Radislav Vaisman
Publisher : CRC Press
Page : 538 pages
File Size : 46,6 Mb
Release : 2019-11-20
Category : Business & Economics
ISBN : 9781000730777

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Data Science and Machine Learning by Dirk P. Kroese,Zdravko Botev,Thomas Taimre,Radislav Vaisman Pdf

Focuses on mathematical understanding Presentation is self-contained, accessible, and comprehensive Full color throughout Extensive list of exercises and worked-out examples Many concrete algorithms with actual code

Data Science

Francesco Palumbo,Angela Montanari,Maurizio Vichi
Data Science Book PDF

Author : Francesco Palumbo,Angela Montanari,Maurizio Vichi
Publisher : Springer
Page : 342 pages
File Size : 53,5 Mb
Release : 2017-07-04
Category : Mathematics
ISBN : 9783319557236

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Data Science by Francesco Palumbo,Angela Montanari,Maurizio Vichi Pdf

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.

Data Science, Classification, and Related Methods

International Federation of Classification Societies. Conference
Data Science Classification and Related Methods Book PDF

Author : International Federation of Classification Societies. Conference
Publisher : Springer
Page : 808 pages
File Size : 45,5 Mb
Release : 1998-03
Category : Business & Economics
ISBN : UCSC:32106013767527

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Data Science, Classification, and Related Methods by International Federation of Classification Societies. Conference Pdf

This volume contains selected papers covering a wide range of topics, including theoretical and methodological advances relating to data gathering, classification and clustering, exploratory and multivariate data analysis, and knowledge seeking and discovery. The result is a broad view of the state of the art, making this an essential work not only for data analysts, mathematicians, and statisticians, but also for researchers involved in data processing at all stages from data gathering to decision making.

Time Series Clustering and Classification

Elizabeth Ann Maharaj,Pierpaolo D'Urso,Jorge Caiado
Time Series Clustering and Classification Book PDF

Author : Elizabeth Ann Maharaj,Pierpaolo D'Urso,Jorge Caiado
Publisher : CRC Press
Page : 213 pages
File Size : 40,7 Mb
Release : 2019-03-19
Category : Mathematics
ISBN : 9780429603303

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Time Series Clustering and Classification by Elizabeth Ann Maharaj,Pierpaolo D'Urso,Jorge Caiado Pdf

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

Data Analysis, Classification, and Related Methods

Henk A.L. Kiers,Jean-Paul Rasson,Patrick J.F. Groenen,Martin Schader
Data Analysis Classification and Related Methods Book PDF

Author : Henk A.L. Kiers,Jean-Paul Rasson,Patrick J.F. Groenen,Martin Schader
Publisher : Springer Science & Business Media
Page : 428 pages
File Size : 52,6 Mb
Release : 2012-12-06
Category : Mathematics
ISBN : 9783642597893

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Data Analysis, Classification, and Related Methods by Henk A.L. Kiers,Jean-Paul Rasson,Patrick J.F. Groenen,Martin Schader Pdf

This volume contains a selection of papers presented at the Seven~h Confer ence of the International Federation of Classification Societies (IFCS-2000), which was held in Namur, Belgium, July 11-14,2000. From the originally sub mitted papers, a careful review process involving two reviewers per paper, led to the selection of 65 papers that were considered suitable for publication in this book. The present book contains original research contributions, innovative ap plications and overview papers in various fields within data analysis, classifi cation, and related methods. Given the fast publication process, the research results are still up-to-date and coincide with their actual presentation at the IFCS-2000 conference. The topics captured are: • Cluster analysis • Comparison of clusterings • Fuzzy clustering • Discriminant analysis • Mixture models • Analysis of relationships data • Symbolic data analysis • Regression trees • Data mining and neural networks • Pattern recognition • Multivariate data analysis • Robust data analysis • Data science and sampling The IFCS (International Federation of Classification Societies) The IFCS promotes the dissemination of technical and scientific information data analysis, classification, related methods, and their applica concerning tions.

Data Science and Classification

Vladimir Batagelj,Hans-Hermann Bock,Anuška Ferligoj,Aleš Žiberna
Data Science and Classification Book PDF

Author : Vladimir Batagelj,Hans-Hermann Bock,Anuška Ferligoj,Aleš Žiberna
Publisher : Springer Science & Business Media
Page : 350 pages
File Size : 41,5 Mb
Release : 2006-09-05
Category : Language Arts & Disciplines
ISBN : 9783540344162

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Data Science and Classification by Vladimir Batagelj,Hans-Hermann Bock,Anuška Ferligoj,Aleš Žiberna Pdf

Data Science and Classification provides new methodological developments in data analysis and classification. The broad and comprehensive coverage includes the measurement of similarity and dissimilarity, methods for classification and clustering, network and graph analyses, analysis of symbolic data, and web mining. Beyond structural and theoretical results, the book offers application advice for a variety of problems, in medicine, microarray analysis, social network structures, and music.

Supervised and Unsupervised Learning for Data Science

Michael W. Berry,Azlinah Mohamed,Bee Wah Yap
Supervised and Unsupervised Learning for Data Science Book PDF

Author : Michael W. Berry,Azlinah Mohamed,Bee Wah Yap
Publisher : Springer Nature
Page : 191 pages
File Size : 44,5 Mb
Release : 2019-09-04
Category : Technology & Engineering
ISBN : 9783030224752

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Supervised and Unsupervised Learning for Data Science by Michael W. Berry,Azlinah Mohamed,Bee Wah Yap Pdf

This book covers the state of the art in learning algorithms with an inclusion of semi-supervised methods to provide a broad scope of clustering and classification solutions for big data applications. Case studies and best practices are included along with theoretical models of learning for a comprehensive reference to the field. The book is organized into eight chapters that cover the following topics: discretization, feature extraction and selection, classification, clustering, topic modeling, graph analysis and applications. Practitioners and graduate students can use the volume as an important reference for their current and future research and faculty will find the volume useful for assignments in presenting current approaches to unsupervised and semi-supervised learning in graduate-level seminar courses. The book is based on selected, expanded papers from the Fourth International Conference on Soft Computing in Data Science (2018). Includes new advances in clustering and classification using semi-supervised and unsupervised learning; Address new challenges arising in feature extraction and selection using semi-supervised and unsupervised learning; Features applications from healthcare, engineering, and text/social media mining that exploit techniques from semi-supervised and unsupervised learning.

Computational Learning Approaches to Data Analytics in Biomedical Applications

Khalid Al-Jabery,Tayo Obafemi-Ajayi,Gayla Olbricht,Donald Wunsch
Computational Learning Approaches to Data Analytics in Biomedical Applications Book PDF

Author : Khalid Al-Jabery,Tayo Obafemi-Ajayi,Gayla Olbricht,Donald Wunsch
Publisher : Academic Press
Page : 312 pages
File Size : 44,9 Mb
Release : 2019-11-20
Category : Technology & Engineering
ISBN : 9780128144831

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Computational Learning Approaches to Data Analytics in Biomedical Applications by Khalid Al-Jabery,Tayo Obafemi-Ajayi,Gayla Olbricht,Donald Wunsch Pdf

Computational Learning Approaches to Data Analytics in Biomedical Applications provides a unified framework for biomedical data analysis using varied machine learning and statistical techniques. It presents insights on biomedical data processing, innovative clustering algorithms and techniques, and connections between statistical analysis and clustering. The book introduces and discusses the major problems relating to data analytics, provides a review of influential and state-of-the-art learning algorithms for biomedical applications, reviews cluster validity indices and how to select the appropriate index, and includes an overview of statistical methods that can be applied to increase confidence in the clustering framework and analysis of the results obtained. Includes an overview of data analytics in biomedical applications and current challenges Updates on the latest research in supervised learning algorithms and applications, clustering algorithms and cluster validation indices Provides complete coverage of computational and statistical analysis tools for biomedical data analysis Presents hands-on training on the use of Python libraries, MATLAB® tools, WEKA, SAP-HANA and R/Bioconductor