rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Core Data Analysis: Summarization, Correlation, and Visualization
  name:
    name part: Mirkin, Boris.
    role:
      role term: creator
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2019
    edition: 2nd ed. 2019.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XV, 524 p. 187 illus., 80 illus. in color. online resource.
  abstract: 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. .
  table of contents: Topics in Data Analysis Substance -- Quantitative Summarization -- Learning Correlations -- Core Partitioning: K-Means and Similarity Clustering -- Divisive and Separate Cluster Structures -- Appendix. Basic Math and Code -- Index.
  note: by Boris Mirkin.
  subject:
    topic: Data structures (Computer science)
  subject:
    topic: Computer security
  subject:
    topic: Data mining
  subject:
    topic: Computer science-Mathematics
  subject:
    topic: Data Structures
  subject:
    topic: Systems and Data Security
  subject:
    topic: Data Mining and Knowledge Discovery
  subject:
    topic: Math Applications in Computer Science
  classification: QA76.9.D35
  classification: 005.73
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Undergraduate Topics in Computer Science
  identifier: 9783030002718
  identifier: https://doi.org/10.1007/978-3-030-00271-8
  record info:
    record creation date: 190415
    record change date: 20191025162017.0
    record identifier: 978-3-030-00271-8