Visual Data Mining  Theory, Techniques and Tools for Visual Analytics /  [electronic resource] : 
edited by Simeon Simoff, Michael H. Böhlen, Arturas Mazeika. 
 - 1st ed. 2008. 
 - X, 407 p.  online resource. 
 - Information Systems and Applications, incl. Internet/Web, and HCI,  4404  2946-1642 ; .
 - Information Systems and Applications, incl. Internet/Web, and HCI,  4404 .
<br/><br/>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. 
<br/><br/> 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. 
<br/><br/><label>ISBN: </label>9783540710806 
<br/><br/><label>Standard No.: </label>10.1007/978-3-540-71080-6  doi 
<br/><br/><label>Subjects--Topical Terms: </label><br/>Data mining.<br/>Computer graphics.<br/>Database management.<br/>Information storage and retrieval systems.<br/>Data Mining and Knowledge Discovery.<br/>Computer Graphics.<br/>Database Management.<br/>Information Storage and Retrieval.
<br/><br/><label>LC Class. No.: </label>QA76.9.D343 
<br/><br/><label>Dewey Class. No.: </label>006.312