Core Data Analysis Summarization Correlation And Visualization

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Core Data Analysis Summarization Correlation And Visualization

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Author : Boris Mirkin
language : en
Publisher: Springer
Release Date : 2019-04-15

Core Data Analysis Summarization Correlation And Visualization written by Boris Mirkin and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-04-15 with Computers categories.

This text examines the goals of data analysis with respect to enhancing knowledge, and identifies data summarization and correlation analysis as the core issues. Data summarization, both quantitative and categorical, is treated within the encoder-decoder paradigm bringing forward a number of mathematically supported insights into the methods and relations between them. Two Chapters describe methods for categorical summarization: partitioning, divisive clustering and separate cluster finding and another explain the methods for quantitative summarization, Principal Component Analysis and PageRank. Features: · An in-depth presentation of K-means partitioning including a corresponding Pythagorean decomposition of the data scatter. · Advice regarding such issues as clustering of categorical and mixed scale data, similarity and network data, interpretation aids, anomalous clusters, the number of clusters, etc. · Thorough attention to data-driven modelling including a number of mathematically stated relations between statistical and geometrical concepts including those between goodness-of-fit criteria for decision trees and data standardization, similarity and consensus clustering, modularity clustering and uniform partitioning. New edition highlights: · Inclusion of ranking issues such as Google PageRank, linear stratification and tied rankings median, consensus clustering, semi-average clustering, one-cluster clustering · Restructured to make the logics more straightforward and sections self-contained Core Data Analysis: Summarization, Correlation and Visualization is aimed at those who are eager to participate in developing the field as well as appealing to novices and practitioners.

Core Concepts In Data Analysis Summarization Correlation And Visualization

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Author : Boris Mirkin
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-04-05

Core Concepts In Data Analysis Summarization Correlation And Visualization written by Boris Mirkin and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-04-05 with Computers categories.

Core Concepts in Data Analysis: Summarization, Correlation and Visualization provides in-depth descriptions of those data analysis approaches that either summarize data (principal component analysis and clustering, including hierarchical and network clustering) or correlate different aspects of data (decision trees, linear rules, neuron networks, and Bayes rule). Boris Mirkin takes an unconventional approach and introduces the concept of multivariate data summarization as a counterpart to conventional machine learning prediction schemes, utilizing techniques from statistics, data analysis, data mining, machine learning, computational intelligence, and information retrieval. Innovations following from his in-depth analysis of the models underlying summarization techniques are introduced, and applied to challenging issues such as the number of clusters, mixed scale data standardization, interpretation of the solutions, as well as relations between seemingly unrelated concepts: goodness-of-fit functions for classification trees and data standardization, spectral clustering and additive clustering, correlation and visualization of contingency data. The mathematical detail is encapsulated in the so-called “formulation” parts, whereas most material is delivered through “presentation” parts that explain the methods by applying them to small real-world data sets; concise “computation” parts inform of the algorithmic and coding issues. Four layers of active learning and self-study exercises are provided: worked examples, case studies, projects and questions.

Core Concepts In Data Analysis Summarization Correlation And Visualization

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Author : Boris Mirkin
language : en
Publisher: Springer
Release Date : 2011-04-07

Core Concepts In Data Analysis Summarization Correlation And Visualization written by Boris Mirkin and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-04-07 with Computers categories.

Core Concepts in Data Analysis: Summarization, Correlation and Visualization provides in-depth descriptions of those data analysis approaches that either summarize data (principal component analysis and clustering, including hierarchical and network clustering) or correlate different aspects of data (decision trees, linear rules, neuron networks, and Bayes rule). Boris Mirkin takes an unconventional approach and introduces the concept of multivariate data summarization as a counterpart to conventional machine learning prediction schemes, utilizing techniques from statistics, data analysis, data mining, machine learning, computational intelligence, and information retrieval. Innovations following from his in-depth analysis of the models underlying summarization techniques are introduced, and applied to challenging issues such as the number of clusters, mixed scale data standardization, interpretation of the solutions, as well as relations between seemingly unrelated concepts: goodness-of-fit functions for classification trees and data standardization, spectral clustering and additive clustering, correlation and visualization of contingency data. The mathematical detail is encapsulated in the so-called “formulation” parts, whereas most material is delivered through “presentation” parts that explain the methods by applying them to small real-world data sets; concise “computation” parts inform of the algorithmic and coding issues. Four layers of active learning and self-study exercises are provided: worked examples, case studies, projects and questions.

Applications Of Artificial Intelligence In Covid 19

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Author : Sachi Nandan Mohanty
language : en
Publisher: Springer Nature
Release Date : 2021-09-29

Applications Of Artificial Intelligence In Covid 19 written by Sachi Nandan Mohanty and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-29 with Medical categories.

The book examines the role of artificial intelligence during the COVID-19 pandemic, including its application in i) early warnings and alerts, ii) tracking and prediction, iii) data dashboards, iv) diagnosis and prognosis, v) treatments, and cures, and vi) social control. It explores the use of artificial intelligence in the context of population screening and assessing infection risks, and presents mathematical models for epidemic prediction of COVID-19. Furthermore, the book discusses artificial intelligence-mediated diagnosis, and how machine learning can help in the development of drugs to treat the disease. Lastly, it analyzes various artificial intelligence-based models to improve the critical care of COVID-19 patients.

Data Analysis And Optimization

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Author : Boris Goldengorin
language : en
Publisher: Springer Nature
Release Date : 2023-09-23

Data Analysis And Optimization written by Boris Goldengorin and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-23 with Computers categories.

This book presents the state-of-the-art in the emerging field of data science and includes models for layered security with applications in the protection of sites—such as large gathering places—through high-stake decision-making tasks. Such tasks include cancer diagnostics, self-driving cars, and others where wrong decisions can possibly have catastrophic consequences. Additionally, this book provides readers with automated methods to analyze patterns and models for various types of data, with applications ranging from scientific discovery to business intelligence and analytics. The book primarily includes exploratory data analysis, pattern mining, clustering, and classification supported by real life case studies. The statistical section of this book explores the impact of data mining and modeling on the predictability assessment of time series. Further new notions of mean values based on ideas of multi-criteria optimization are compared with their conventional definitions, leading to new algorithmic approaches to the calculation of the suggested new means. The style of the written chapters and the provision of a broad yet in-depth overview of data mining, integrating novel concepts from machine learning and statistics, make the book accessible to upper level undergraduate and graduate students in data mining courses. Students and professionals specializing in computer and management science, data mining for high-dimensional data, complex graphs and networks will benefit from the cutting-edge ideas and practically motivated case studies in this book.

Database And Expert Systems Applications

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Author : Sven Hartmann
language : en
Publisher: Springer Nature
Release Date : 2020-09-13

Database And Expert Systems Applications written by Sven Hartmann and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-09-13 with Computers categories.

The double volumes LNCS 12391-12392 constitutes the papers of the 31st International Conference on Database and Expert Systems Applications, DEXA 2020, which will be held online in September 2020. The 38 full papers presented together with 20 short papers plus 1 keynote papers in these volumes were carefully reviewed and selected from a total of 190 submissions.

Proceedings Of The Second International Conference On Advances In Computing Research Acr 24

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Author : Kevin Daimi
language : en
Publisher: Springer Nature
Release Date :

Proceedings Of The Second International Conference On Advances In Computing Research Acr 24 written by Kevin Daimi and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.

Clusters Orders And Trees Methods And Applications

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Author : Fuad Aleskerov
language : en
Publisher: Springer
Release Date : 2014-06-11

Clusters Orders And Trees Methods And Applications written by Fuad Aleskerov and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-06-11 with Mathematics categories.

The volume is dedicated to Boris Mirkin on the occasion of his 70th birthday. In addition to his startling PhD results in abstract automata theory, Mirkin’s ground breaking contributions in various fields of decision making and data analysis have marked the fourth quarter of the 20th century and beyond. Mirkin has done pioneering work in group choice, clustering, data mining and knowledge discovery aimed at finding and describing non-trivial or hidden structures—first of all, clusters, orderings and hierarchies—in multivariate and/or network data. This volume contains a collection of papers reflecting recent developments rooted in Mirkin’s fundamental contribution to the state-of-the-art in group choice, ordering, clustering, data mining and knowledge discovery. Researchers, students and software engineers will benefit from new knowledge discovery techniques and application directions.

Advances In Intelligent Data Analysis Xi

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Author : Jaakko Hollmen
language : en
Publisher: Springer
Release Date : 2012-10-20

Advances In Intelligent Data Analysis Xi written by Jaakko Hollmen and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-10-20 with Computers categories.

This book constitutes the refereed proceedings of the 11th International Conference on Intelligent Data Analysis, IDA 2012, held in Helsinki, Finland, in October 2012. The 32 revised full papers presented together with 3 invited papers were carefully reviewed and selected from 88 submissions. All current aspects of intelligent data analysis are addressed, including intelligent support for modeling and analyzing data from complex, dynamical systems. The papers focus on novel applications of IDA techniques to, e.g., networked digital information systems; novel modes of data acquisition and the associated issues; robustness and scalability issues of intelligent data analysis techniques; and visualization and dissemination results.

Interactive Knowledge Discovery And Data Mining In Biomedical Informatics

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Author : Andreas Holzinger
language : en
Publisher: Springer
Release Date : 2014-06-17

Interactive Knowledge Discovery And Data Mining In Biomedical Informatics written by Andreas Holzinger and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-06-17 with Computers categories.

One of the grand challenges in our digital world are the large, complex and often weakly structured data sets, and massive amounts of unstructured information. This “big data” challenge is most evident in biomedical informatics: the trend towards precision medicine has resulted in an explosion in the amount of generated biomedical data sets. Despite the fact that human experts are very good at pattern recognition in dimensions of = 3; most of the data is high-dimensional, which makes manual analysis often impossible and neither the medical doctor nor the biomedical researcher can memorize all these facts. A synergistic combination of methodologies and approaches of two fields offer ideal conditions towards unraveling these problems: Human–Computer Interaction (HCI) and Knowledge Discovery/Data Mining (KDD), with the goal of supporting human capabilities with machine learning./ppThis state-of-the-art survey is an output of the HCI-KDD expert network and features 19 carefully selected and reviewed papers related to seven hot and promising research areas: Area 1: Data Integration, Data Pre-processing and Data Mapping; Area 2: Data Mining Algorithms; Area 3: Graph-based Data Mining; Area 4: Entropy-Based Data Mining; Area 5: Topological Data Mining; Area 6 Data Visualization and Area 7: Privacy, Data Protection, Safety and Security.