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...
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| Other Authors: | , , |
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Berlin, Heidelberg :
Springer Berlin Heidelberg : Imprint: Springer,
2008.
|
| Edition: | 1st ed. 2008. |
| Series: | Information Systems and Applications, incl. Internet/Web, and HCI ;
4404 |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/978-3-540-71080-6 |
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| 001 | 978-3-540-71080-6 | ||
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| 020 | |a 9783540710806 |9 978-3-540-71080-6 | ||
| 024 | 7 | |a 10.1007/978-3-540-71080-6 |2 doi | |
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| 082 | 0 | 4 | |a 006.312 |2 23 |
| 245 | 1 | 0 | |a Visual Data Mining |h [electronic resource] : |b Theory, Techniques and Tools for Visual Analytics / |c edited by Simeon Simoff, Michael H. Böhlen, Arturas Mazeika. |
| 250 | |a 1st ed. 2008. | ||
| 264 | 1 | |a Berlin, Heidelberg : |b Springer Berlin Heidelberg : |b Imprint: Springer, |c 2008. | |
| 300 | |a X, 407 p. |b online resource. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a text file |b PDF |2 rda | ||
| 490 | 1 | |a Information Systems and Applications, incl. Internet/Web, and HCI ; |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. | ||
| 650 | 0 | |a Data mining. | |
| 650 | 0 | |a Computer graphics. | |
| 650 | 0 | |a Database management. | |
| 650 | 0 | |a Information storage and retrieval. | |
| 650 | 1 | 4 | |a Data Mining and Knowledge Discovery. |0 https://scigraph.springernature.com/ontologies/product-market-codes/I18030 |
| 650 | 2 | 4 | |a Computer Graphics. |0 https://scigraph.springernature.com/ontologies/product-market-codes/I22013 |
| 650 | 2 | 4 | |a Database Management. |0 https://scigraph.springernature.com/ontologies/product-market-codes/I18024 |
| 650 | 2 | 4 | |a Information Storage and Retrieval. |0 https://scigraph.springernature.com/ontologies/product-market-codes/I18032 |
| 700 | 1 | |a Simoff, Simeon. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Böhlen, Michael H. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Mazeika, Arturas. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 710 | 2 | |a SpringerLink (Online service) | |
| 773 | 0 | |t Springer Nature eBook | |
| 776 | 0 | 8 | |i Printed edition: |z 9783540867517 |
| 776 | 0 | 8 | |i Printed edition: |z 9783540710790 |
| 830 | 0 | |a Information Systems and Applications, incl. Internet/Web, and HCI ; |v 4404 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/978-3-540-71080-6 |
| 912 | |a ZDB-2-SCS | ||
| 912 | |a ZDB-2-SXCS | ||
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| 950 | |a Computer Science (SpringerNature-11645) | ||
| 950 | |a Computer Science (R0) (SpringerNature-43710) | ||