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: Visual Data Mining
    sub title: Theory, Techniques and Tools for Visual Analytics
  name:
    name part: Simoff, Simeon J.
    role:
      role term: editor.
  name:
    name part: Böhlen, Michael H.
    role:
      role term: editor.
  name:
    name part: Mazeika, Arturas.
    role:
      role term: editor.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2008
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: X, 407 p. online resource.
  abstract: The importance of visual data mining, as a strong sub-discipline of data mining, had already been recognized in the beginning of the decade. In 2005 a panel of renowned individuals met to address the shortcomings and drawbacks of the current state of visual information processing. The need for a systematic and methodological development of visual analytics was detected. This book aims at addressing this need. Through a collection of 21 contributions selected from more than 46 submissions, it offers a systematic presentation of the state of the art in the field. The volume is structured in three parts on theory and methodologies, techniques, and tools and applications.
  table of contents: Visual Data Mining: An Introduction and Overview -- Visual Data Mining: An Introduction and Overview -- 1 – Theory and Methodologies -- The 3DVDM Approach: A Case Study with Clickstream Data -- Form-Semantics-Function – A Framework for Designing Visual Data Representations for Visual Data Mining -- A Methodology for Exploring Association Models -- Visual Exploration of Frequent Itemsets and Association Rules -- Visual Analytics: Scope and Challenges -- 2 – Techniques -- Using Nested Surfaces for Visual Detection of Structures in Databases -- Visual Mining of Association Rules -- Interactive Decision Tree Construction for Interval and Taxonomical Data -- Visual Methods for Examining SVM Classifiers -- Text Visualization for Visual Text Analytics -- Visual Discovery of Network Patterns of Interaction between Attributes -- Mining Patterns for Visual Interpretation in a Multiple-Views Environment -- Using 2D Hierarchical Heavy Hitters to Investigate Binary Relationships -- Complementing Visual Data Mining with the Sound Dimension: Sonification of Time Dependent Data -- Context Visualization for Visual Data Mining -- Assisting Human Cognition in Visual Data Mining -- 3 – Tools and Applications -- Immersive Visual Data Mining: The 3DVDM Approach -- DataJewel: Integrating Visualization with Temporal Data Mining -- A Visual Data Mining Environment -- Integrative Visual Data Mining of Biomedical Data: Investigating Cases in Chronic Fatigue Syndrome and Acute Lymphoblastic Leukaemia -- Towards Effective Visual Data Mining with Cooperative Approaches.
  note: edited by Simeon J. Simoff, Michael H. Böhlen, Arturas Mazeika.
  subject:
    topic: Computer science
  subject:
    topic: Database management
  subject:
    topic: Data mining
  subject:
    topic: Information storage and retrieval
  subject:
    topic: Computer graphics
  subject:
    topic: Computer Science
  subject:
    topic: Data Mining and Knowledge Discovery
  subject:
    topic: Computer Graphics
  subject:
    topic: Database Management
  subject:
    topic: Information Storage and Retrieval
  classification: QA76.9.D343
  classification: 006.312
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Lecture Notes in Computer Science, 4404
  identifier: 9783540710806
  identifier: http://dx.doi.org/10.1007/978-3-540-71080-6
  record info:
    record creation date: 100301
    record change date: 20151204162637.0
    record identifier: 978-3-540-71080-6