Braque: design of an interface to support user interaction in information retrieval
Information Processing and Management: an International Journal - Special issue on hypertext and information retrieval
Information behaviour: an inter-disciplinary perspective
ISIC '96 Proceedings of an international conference on Information seeking in context
Information Visualization and Visual Data Mining
IEEE Transactions on Visualization and Computer Graphics
The challenge of information visualization evaluation
Proceedings of the working conference on Advanced visual interfaces
Scientific data management in the coming decade
ACM SIGMOD Record
Scholarship in the Digital Age: Information, Infrastructure, and the Internet
Scholarship in the Digital Age: Information, Infrastructure, and the Internet
Visual Data Mining: Theory, Techniques and Tools for Visual Analytics
Visual Data Mining: Theory, Techniques and Tools for Visual Analytics
Visual Analytics: Scope and Challenges
Visual Data Mining
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Visual mining refers to the cognitive process which integrates the human in analysis of information when using interactive visualization systems. This paper presents a classification scheme which provides user-centered representation of goals and actions that a user performs during the visual mining process. The classification scheme has been developed using contentanalysis of published literature containing precise descriptions of different visual mining tasks in multiple fields of study. There were two stages in the development. First, we defined all the sub-processes of visual mining process. Then we used these sub-processes as a template to develop the initial coding scheme prior to utilizing specific data from each of the publications. As analysis proceeded, additional codes were developed and the initial coding scheme was refined. The results of the analysis were represented in the form of a classification scheme of the visual mining process. The naturalistic methods recommended by Lincoln and Guba have been applied to ensure that the content analysis is credible, transferable, dependable and confirmable.