That the already vast and ever-increasing amounts of data still do present formidable challenges to effective and efficient acquisition of knowledge is by no means an exaggeration. The knowledge discovery process entails more than just the application of data mining strategies. There are many other aspects including, but not limited to: planning, data pre-processing, data integration, evaluation and presentation. The human-vision channel is capable of recognizing and understanding data at an instant. Effective visual strategies can be used to tap the outstanding human visual channel in extracting useful information from data. Unlike is the case with most research efforts, the exploitation should be employed not just at the beginning or at the end of the knowledge discovery process but across the entire discovery process. In essence, this calls for the development of an effective user/visual component, the development of an overall framework that can support the entire discovery process/all discovery phases, and the strategic placement of the visual component in that framework. Key issues of this component will be the open architecture, allowing extensions and adaptations to specific mining environments, and the precise semantics and syntax, allowing an optimal integration between the presentation and the computation.

VidaMine: A Visual Data Mining Environment

LODI, STEFANO;SARTORI, CLAUDIO
2004

Abstract

That the already vast and ever-increasing amounts of data still do present formidable challenges to effective and efficient acquisition of knowledge is by no means an exaggeration. The knowledge discovery process entails more than just the application of data mining strategies. There are many other aspects including, but not limited to: planning, data pre-processing, data integration, evaluation and presentation. The human-vision channel is capable of recognizing and understanding data at an instant. Effective visual strategies can be used to tap the outstanding human visual channel in extracting useful information from data. Unlike is the case with most research efforts, the exploitation should be employed not just at the beginning or at the end of the knowledge discovery process but across the entire discovery process. In essence, this calls for the development of an effective user/visual component, the development of an overall framework that can support the entire discovery process/all discovery phases, and the strategic placement of the visual component in that framework. Key issues of this component will be the open architecture, allowing extensions and adaptations to specific mining environments, and the precise semantics and syntax, allowing an optimal integration between the presentation and the computation.
LODI S; CATARCI T; SANTUCCI G; SARTORI C.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/2578
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