A Framework for Visual Data Mining of Structures

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Standard

A Framework for Visual Data Mining of Structures. / Schulz, Hans-Jörg; Nocke, Thomas; Schumann, Heidrun.

In: Conferences in Research and Practice in Information Technology, Vol. 48, 2006, p. 157-166.

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperConference articleResearchpeer-review

Harvard

Schulz, H-J, Nocke, T & Schumann, H 2006, 'A Framework for Visual Data Mining of Structures', Conferences in Research and Practice in Information Technology, vol. 48, pp. 157-166. <http://crpit.com/abstracts/CRPITV48Schulz.html>

APA

Schulz, H-J., Nocke, T., & Schumann, H. (2006). A Framework for Visual Data Mining of Structures. Conferences in Research and Practice in Information Technology, 48, 157-166. http://crpit.com/abstracts/CRPITV48Schulz.html

CBE

Schulz H-J, Nocke T, Schumann H. 2006. A Framework for Visual Data Mining of Structures. Conferences in Research and Practice in Information Technology. 48:157-166.

MLA

Schulz, Hans-Jörg, Thomas Nocke and Heidrun Schumann. "A Framework for Visual Data Mining of Structures". Conferences in Research and Practice in Information Technology. 2006, 48. 157-166.

Vancouver

Schulz H-J, Nocke T, Schumann H. A Framework for Visual Data Mining of Structures. Conferences in Research and Practice in Information Technology. 2006;48:157-166.

Author

Schulz, Hans-Jörg ; Nocke, Thomas ; Schumann, Heidrun. / A Framework for Visual Data Mining of Structures. In: Conferences in Research and Practice in Information Technology. 2006 ; Vol. 48. pp. 157-166.

Bibtex

@inproceedings{72d8f3f4ad26493e981bb92612f7c5d4,
title = "A Framework for Visual Data Mining of Structures",
abstract = "Visual data mining has been established to effectively analyze large, complex numerical data sets. Especially, the extraction and visualization of inherent structures such as hierarchies and networks has made a significant leap forward. However, it is still a challenging task for users to explore explicitly given large structures. In this paper, we approach this task by tightly coupling visualization and graph-theoretical methods. Therefore, we investigate if and how visualization can benefit from common graph-theoretical methods - mainly developed for the investigation of social networks - and vice versa. To accomplish this close integration, we introduce a design of a general framework for visual data mining of complex structures. Especially, this design includes an appropriate processing order of different mining and visualization algorithms and their mining results. Furthermore, we discuss some important implementation details of our framework to ensure fast structure processing. Finally, we examine the applicability of the framework for a large real-world data set.",
author = "Hans-J{\"o}rg Schulz and Thomas Nocke and Heidrun Schumann",
year = "2006",
language = "English",
volume = "48",
pages = "157--166",
journal = "Nyt fra Arbejdsministeriet",

}

RIS

TY - GEN

T1 - A Framework for Visual Data Mining of Structures

AU - Schulz, Hans-Jörg

AU - Nocke, Thomas

AU - Schumann, Heidrun

PY - 2006

Y1 - 2006

N2 - Visual data mining has been established to effectively analyze large, complex numerical data sets. Especially, the extraction and visualization of inherent structures such as hierarchies and networks has made a significant leap forward. However, it is still a challenging task for users to explore explicitly given large structures. In this paper, we approach this task by tightly coupling visualization and graph-theoretical methods. Therefore, we investigate if and how visualization can benefit from common graph-theoretical methods - mainly developed for the investigation of social networks - and vice versa. To accomplish this close integration, we introduce a design of a general framework for visual data mining of complex structures. Especially, this design includes an appropriate processing order of different mining and visualization algorithms and their mining results. Furthermore, we discuss some important implementation details of our framework to ensure fast structure processing. Finally, we examine the applicability of the framework for a large real-world data set.

AB - Visual data mining has been established to effectively analyze large, complex numerical data sets. Especially, the extraction and visualization of inherent structures such as hierarchies and networks has made a significant leap forward. However, it is still a challenging task for users to explore explicitly given large structures. In this paper, we approach this task by tightly coupling visualization and graph-theoretical methods. Therefore, we investigate if and how visualization can benefit from common graph-theoretical methods - mainly developed for the investigation of social networks - and vice versa. To accomplish this close integration, we introduce a design of a general framework for visual data mining of complex structures. Especially, this design includes an appropriate processing order of different mining and visualization algorithms and their mining results. Furthermore, we discuss some important implementation details of our framework to ensure fast structure processing. Finally, we examine the applicability of the framework for a large real-world data set.

M3 - Conference article

VL - 48

SP - 157

EP - 166

JO - Nyt fra Arbejdsministeriet

JF - Nyt fra Arbejdsministeriet

ER -