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

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 sys...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Άλλοι συγγραφείς: Simoff, Simeon J. (Επιμελητής έκδοσης), Böhlen, Michael H. (Επιμελητής έκδοσης), Mazeika, Arturas (Επιμελητής έκδοσης)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg, 2008.
Σειρά:Lecture Notes in Computer Science, 4404
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
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245 1 0 |a Visual Data Mining  |h [electronic resource] :  |b Theory, Techniques and Tools for Visual Analytics /  |c edited by Simeon J. Simoff, Michael H. Böhlen, Arturas Mazeika. 
264 1 |a Berlin, Heidelberg :  |b Springer Berlin Heidelberg,  |c 2008. 
300 |a X, 407 p.  |b online resource. 
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490 1 |a Lecture Notes in Computer Science,  |x 0302-9743 ;  |v 4404 
505 0 |a 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. 
520 |a 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. 
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650 0 |a Database management. 
650 0 |a Data mining. 
650 0 |a Information storage and retrieval. 
650 0 |a Computer graphics. 
650 1 4 |a Computer Science. 
650 2 4 |a Data Mining and Knowledge Discovery. 
650 2 4 |a Computer Graphics. 
650 2 4 |a Database Management. 
650 2 4 |a Information Storage and Retrieval. 
700 1 |a Simoff, Simeon J.  |e editor. 
700 1 |a Böhlen, Michael H.  |e editor. 
700 1 |a Mazeika, Arturas.  |e editor. 
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776 0 8 |i Printed edition:  |z 9783540710790 
830 0 |a Lecture Notes in Computer Science,  |x 0302-9743 ;  |v 4404 
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