The Resource Visual data mining : theory, techniques and tools for visual analytics, Simeon J. Simoff, Michael H. Böhlen, Arturas Mazeika (eds.), (electronic resource)

Visual data mining : theory, techniques and tools for visual analytics, Simeon J. Simoff, Michael H. Böhlen, Arturas Mazeika (eds.), (electronic resource)

Label
Visual data mining : theory, techniques and tools for visual analytics
Title
Visual data mining
Title remainder
theory, techniques and tools for visual analytics
Statement of responsibility
Simeon J. Simoff, Michael H. Böhlen, Arturas Mazeika (eds.)
Creator
Contributor
Provider
Subject
Language
eng
Summary
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
Member of
Cataloging source
GW5XE
http://library.link/vocab/creatorDate
1962-
http://library.link/vocab/creatorName
Simoff, Simeon J.
Image bit depth
0
LC call number
T385
LC item number
.S548 2008eb
Literary form
non fiction
Nature of contents
dictionaries
http://library.link/vocab/relatedWorkOrContributorDate
1964-
http://library.link/vocab/relatedWorkOrContributorName
  • SpringerLink
  • Böhlen, Michael H.
  • Mazeika, Arturas
Series statement
Lecture Notes in Computer Science,
Series volume
4404
http://library.link/vocab/subjectName
  • Computer graphics
  • Data mining
  • Information storage and retrieval systems
  • Computer science
  • Computer graphics
  • Computer science
  • Data mining
  • Information storage and retrieval systems
  • Informatique
Label
Visual data mining : theory, techniques and tools for visual analytics, Simeon J. Simoff, Michael H. Böhlen, Arturas Mazeika (eds.), (electronic resource)
Instantiates
Publication
Antecedent source
mixed
Bibliography note
Includes bibliographical references and index
Color
not applicable
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
Dimensions
unknown
Extent
1 online resource (x, 406 p.)
File format
multiple file formats
Form of item
electronic
Isbn
9783540710806
Level of compression
uncompressed
Other physical details
ill.
Quality assurance targets
absent
Reformatting quality
access
Specific material designation
remote
Stock number
978-3-540-71079-0
System control number
  • (OCoLC)272298876
  • (OCoLC)ocn272298876
  • (DE-He213)978-3-540-71080-6
Label
Visual data mining : theory, techniques and tools for visual analytics, Simeon J. Simoff, Michael H. Böhlen, Arturas Mazeika (eds.), (electronic resource)
Publication
Antecedent source
mixed
Bibliography note
Includes bibliographical references and index
Color
not applicable
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
Dimensions
unknown
Extent
1 online resource (x, 406 p.)
File format
multiple file formats
Form of item
electronic
Isbn
9783540710806
Level of compression
uncompressed
Other physical details
ill.
Quality assurance targets
absent
Reformatting quality
access
Specific material designation
remote
Stock number
978-3-540-71079-0
System control number
  • (OCoLC)272298876
  • (OCoLC)ocn272298876
  • (DE-He213)978-3-540-71080-6

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