Stock photo: cover may vary
Core Data Analysis: Summarization, Correlation, and Visualization (Undergraduate Topics in Computer Science) Paperback - 2019
by Mirkin, Boris
- New
- Paperback
A$111.08
Free Delivery within USA
Standard delivery: 5 to 10 days
More delivery options
Standard delivery: 5 to 10 days
Dropship order
Ships from Ergodebooks (Texas, United States)
Details
- Title Core Data Analysis: Summarization, Correlation, and Visualization (Undergraduate Topics in Computer Science)
- Author Mirkin, Boris
- Binding Paperback
- Edition 2nd ed. 2019
- Condition New
- Pages 524
- Volumes 1
- Language ENG
- Publisher Springer
- Publication date 2019-04-18
- Illustrated Yes
- Features Illustrated
- Bookseller's Inventory # DADAX3030002705
- ISBN 9783030002701 / 3030002705
- Weight 1.65 lbs (0.75 kg)
- Dimensions 9.21 x 6.14 x 1.09 in (23.39 x 15.60 x 2.77 cm)
- Size 6.10x1.22x9.25
- Category Computers - Data Base Management
- Dewey Decimal Code 004.015
- Quantity available 6
About Ergodebooks Texas, United States
Biblio member since 2005
Our goal is to provide best customer service and good condition books for the lowest possible price. We are always honest about condition of book. We list book only by ISBN # and hence exact book is guaranteed.
We have 30 day return policy.
Reader reviews for Core Data Analysis: Summarization, Correlation, and Visualization (Undergraduate Topics in Computer Science)
Write a review for this book
Important Terms and Guidelines
- Please focus on the book’s content and context. Also, add any personal comments as to how you enjoyed the book. Substantiate your likes and dislikes. You may make comparisons to other books.
- Reviews must be at least 140 characters in length.
- Please do not reveal critical plot elements.
- This is not a help line. Contact customer support if you need help.
Your review must not include:
- Obscenities, discriminatory language, or other insulting language not suitable for public domain
- Advertisements, “spam” content, or references to other products, offers or websites.
- Email addresses, URLs, phone numbers, physical addresses or other contact information.
- Overly critical comments about other reviews or reviewers
- Time-sensitive material (i.e. promotional tours, seminars, lectures, etc.)
- Availability, price, or alternative ordering/shipping information
From the publisher
From the rear cover
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.
