Visual Data Mining : Theory, Techniques and Tools for Visual Analytics / edited by Simeon Simoff, Michael H. Böhlen, Arturas Mazeika.

Format:

Book

Contributor:

Simoff, Simeon, editor.

Böhlen, Michael H., 1964- editor.

Mazeika, Arturas, editor.

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

LNCS sublibrary. Information systems and applications, incl. Internet/Web, and HCI ; SL 3, 4404.

Information Systems and Applications, incl. Internet/Web, and HCI ; 4404

Language:

English

Subjects (All):

Data mining.

Computer graphics.

Database management.

Information storage and retrieval.

Data Mining and Knowledge Discovery.

Computer Graphics.

Database Management.

Information Storage and Retrieval.

Local Subjects:

Data Mining and Knowledge Discovery.

Computer Graphics.

Database Management.

Information Storage and Retrieval.

Physical Description:

1 online resource (X, 407 pages).

Edition:

First edition 2008.

Contained In:

Springer eBooks

Place of Publication:

Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2008.

System Details:

text file PDF

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.

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.

Other Format:

Printed edition:

ISBN:

978-3-540-71080-6

9783540710806

Access Restriction:

Restricted for use by site license.