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Visual Data Mining : Theory, Techniques and Tools for Visual Analytics

Author: David HutchisonMichael H BöhlenTakeo KanadeJosef KittlerJon M KleinbergAll authors
Publisher: Berlin, Heidelberg : Springer-Verlag Berlin Heidelberg, 2008.
Series: Lecture notes in computer science, 4404.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Database:WorldCat
Summary:
Annotation 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  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Visual data mining.
Berlin ; New York : Springer, ©2008
(DLC) 2008931578
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: David Hutchison; Michael H Böhlen; Takeo Kanade; Josef Kittler; Jon M Kleinberg; Friedemann Mattern; Arturas Mazeika; John C Mitchell; Moni Naor; Oscar Nierstrasz; C Pandu Rangan; Simeon J Simoff; Bernhard Steffen; Madhu Sudan; Demetri Terzopoulos; Doug Tygar; Moshe Y Vardi; Gerhard Weikum
ISBN: 9783540710806 3540710809 9783540710790 3540710795
OCLC Number: 246899320
Description: 1 online resource (: v.: digital.
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.
Series Title: Lecture notes in computer science, 4404.
Responsibility: edited by David Hutchison, Takeo Kanade, Josef Kittler, Jon M. Kleinberg, Friedemann Mattern, John C. Mitchell, Moni Naor, Oscar Nierstrasz, C. Pandu Rangan, Bernhard Steffen, Madhu Sudan, Demetri Terzopoulos, Doug Tygar, Moshe Y. Vardi, Gerhard Weikum, Simeon J. Simoff, Michael H. Böhlen, Arturas Mazeika.
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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  Read more...

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