Core Data Analysis: Summarization, Correlation, and Visualization

Nonfiction, Computers, Advanced Computing, Programming, Data Modeling & Design, Networking & Communications, Computer Security, General Computing
Cover of the book Core Data Analysis: Summarization, Correlation, and Visualization by Boris Mirkin, Springer International Publishing
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: Boris Mirkin ISBN: 9783030002718
Publisher: Springer International Publishing Publication: April 15, 2019
Imprint: Springer Language: English
Author: Boris Mirkin
ISBN: 9783030002718
Publisher: Springer International Publishing
Publication: April 15, 2019
Imprint: Springer
Language: English

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. 

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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. 

More books from Springer International Publishing

Cover of the book Engineering Applications of Neural Networks by Boris Mirkin
Cover of the book From Biomolecules to Chemofossils by Boris Mirkin
Cover of the book Dynamic Decoupling of Robot Manipulators by Boris Mirkin
Cover of the book Controller Tuning with Evolutionary Multiobjective Optimization by Boris Mirkin
Cover of the book Modern Sensing Technologies by Boris Mirkin
Cover of the book School Funding and Student Achievement by Boris Mirkin
Cover of the book Primate Hearing and Communication by Boris Mirkin
Cover of the book Inverter-Based Circuit Design Techniques for Low Supply Voltages by Boris Mirkin
Cover of the book Proceedings of the Second International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’17) by Boris Mirkin
Cover of the book Model-Free Prediction and Regression by Boris Mirkin
Cover of the book How British Rule Changed India’s Economy by Boris Mirkin
Cover of the book Symmetries and Integrability of Difference Equations by Boris Mirkin
Cover of the book Curating the Digital by Boris Mirkin
Cover of the book Acting to Manage Conflict and Bullying Through Evidence-Based Strategies by Boris Mirkin
Cover of the book Protecting Children Against Bullying and Its Consequences by Boris Mirkin
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy