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Assisting Human Cognition in Visual Data Mining


Simoff, Simeon J; Böhlen, Michael Hanspeter; Mazeika, Arturas (2008). Assisting Human Cognition in Visual Data Mining. In: Simoff, Simeon J; Böhlen, Michael Hanspeter; Mazeika, Arturas. Visual Data Mining: Theory, Techniques and Tools for Visual Analytics. Berlin / Heidelberg: Springer, 264-280.

Abstract

As discussed in Part 1 of the book in chapter Form-Semantics-Function. A Framework for Designing Visualisation Models for Visual Data Mining the development of consistent visualisation techniques requires systematic approach related to the tasks of the visual data mining process. Chapter Visual discovery of network patterns of interaction between attributes presents a methodology based on viewing visual data mining as a reflection-in-action process. This chapter follows the same perspective and focuses on the subjective bias that may appear in visual data mining. The work is motivated by the fact that visual, though very attractive, means also subjective, and non-experts are often left to utilise visualisation methods (as an understandable alternative to the highly complex statistical approaches) without the ability to understand their applicability and limitations. The chapter presents two strategies addressing the subjective bias: guided cognition and validated cognition, which result in two types of visual data mining techniques: interaction with visual data representations, mediated by statistical techniques, and validation of the hypotheses coming as an output of the visual analysis through another analytics method, respectively.

Abstract

As discussed in Part 1 of the book in chapter Form-Semantics-Function. A Framework for Designing Visualisation Models for Visual Data Mining the development of consistent visualisation techniques requires systematic approach related to the tasks of the visual data mining process. Chapter Visual discovery of network patterns of interaction between attributes presents a methodology based on viewing visual data mining as a reflection-in-action process. This chapter follows the same perspective and focuses on the subjective bias that may appear in visual data mining. The work is motivated by the fact that visual, though very attractive, means also subjective, and non-experts are often left to utilise visualisation methods (as an understandable alternative to the highly complex statistical approaches) without the ability to understand their applicability and limitations. The chapter presents two strategies addressing the subjective bias: guided cognition and validated cognition, which result in two types of visual data mining techniques: interaction with visual data representations, mediated by statistical techniques, and validation of the hypotheses coming as an output of the visual analysis through another analytics method, respectively.

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Additional indexing

Item Type:Book Section, refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Language:English
Date:2008
Deposited On:04 Jun 2012 11:15
Last Modified:21 Nov 2017 15:48
Publisher:Springer
Series Name:Lecture Notes in Computer Science
Number: 4404/2008
ISBN:978-3-540-71079-0
Publisher DOI:https://doi.org/10.1007/978-3-540-71080-6_17
Other Identification Number:merlin-id:2319

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