Since the emergence of databases in the 1960s, the volume of stored information has grown exponentially every year (Keim (2002)). This information accumulation in databases has motivated the development of a new research field: Knowledge Discovery in Databases (KDD) (Frawley et al. (1992)) which is commonly defined as the extraction of potentially useful knowledge from data. The KDD process is commonly defined in three stages: pre-processing, Data Mining (DM), and post-processing (Figure 1). At the output of the DM process (post-processing), the decision-maker must evaluate the results and select what is interesting. This task can be improved considerably with visual representations by taking advantage of human capabilities for 3D perception and spatial cognition. Visual representations can allow rapid information recognition and show complex ideas with clarity and efficacy (Card et al. (1999)). In everyday life, we interact with various information media which present us with facts and opinions based on knowledge extracted from data. It is common to communicate such facts and opinions in a virtual form, preferably interactive. For example, when watching weather forecast programs on TV, the icons of a landscape with clouds, rain and sun, allow us to quickly build a picture about the weather forecast. Such a picture is sufficient when we watch the weather forecast, but professional decision-making is a rather different situation. In professional situations, the decision-maker is overwhelmed by the DM algorithm results. Representing these results as static images limits the usefulness of their visualization. This explains why the decision-maker needs to be able to interact with the data representation in order to find relevant knowledge. Visual Data Mining (VDM), presented by Beilken & Spenke (1999) as an interactive visual methodology "to help a user to get a feeling for the data, to detect interesting knowledge, and to gain a deep visual understanding of the data set", can facilitate knowledge discovery in data.
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An Overview of Interaction Techniques and 3D Representations for Data Mining
Semantic Scholar · Computer Science · 2012
Abstract
Since the emergence of databases in the 1960s, the volume of stored information has grown exponentially every year (Keim (2002)). This information accumulation in databases has motivated the development of a new research field: Knowledge Discovery in Databases (KDD) (Frawley et al. (1992)) which is commonly defined as the extraction of potentially useful knowledge from data. The KDD process is commonly defined in three stages: pre-processing, Data Mining (DM), and post-processing (Figure 1). At the output of the DM process (post-processing), the decision-maker must evaluate the results and select what is interesting. This task can be improved considerably with visual representations by taking advantage of human capabilities for 3D perception and spatial cognition. Visual representations can allow rapid information recognition and show complex ideas with clarity and efficacy (Card et al. (1999)). In everyday life, we interact with various information media which present us with facts and opinions based on knowledge extracted from data. It is common to communicate such facts and opinions in a virtual form, preferably interactive. For example, when watching weather forecast programs on TV, the icons of a landscape with clouds, rain and sun, allow us to quickly build a picture about the weather forecast. Such a picture is sufficient when we watch the weather forecast, but professional decision-making is a rather different situation. In professional situations, the decision-maker is overwhelmed by the DM algorithm results. Representing these results as static images limits the usefulness of their visualization. This explains why the decision-maker needs to be able to interact with the data representation in order to find relevant knowledge. Visual Data Mining (VDM), presented by Beilken & Spenke (1999) as an interactive visual methodology "to help a user to get a feeling for the data, to detect interesting knowledge, and to gain a deep visual understanding of the data set", can facilitate knowledge discovery in data.