An Overview of Interaction Techniques and 3D Representations for Data Mining

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 fromdata. 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. In 2D space, VDM has been studied extensively and a number of visualization taxonomies have been proposed (Herman et al. (2000), Chi (2000)). More recently, hardware progress has led to the development of real-time interactive 3D data representation and immersive Virtual Reality (VR) techniques. Thus, aesthetically appealing element inclusion, such as 3D graphics and animation, increases the intuitiveness and memorability of visualization. Also, it eases the perception of the human visual system (Spence (1990), Brath et al. (2005)). Although there is still a debate concerning 2D vs 3D data visualization (Shneiderman (2003)), we believe that 10 2 Will-be-set-by-IN-TECH 3D and VR techniques haves a better potential to assist the decision-maker in analytical tasks, and to deeply immerse the user’s in the data sets. In many cases, the user needs to explore data and/or knowledge fromthe inside-out and not fromthe outside-in, like in 2D techniques (Nelson et al. (1999)). This is only possible in using VR and Virtual Environment (VEs). VEs allow users to navigate continuously to new positions inside the data sets, and thereby obtain more information about the data. Although the benefits offered by VR compared to desk-top 2D and 3D still need to be proven, more and more researchers is investigating its use with VDM (Cai et al. (2007)). In this context, we are trying to develop new 3D visual representations to overcome some limitations of 2D representations. VR has already has been studied in different areas of VDMsuch as pre-processing (Nagel et al. (2008), Ogi et al. (2009)), classification (Einsfeld et al. (2006)), and clustering (Ahmed et al. (2006)). In this context, we review some work that is relevant for researchers seeking or intending to use 3D representation and VR techniques for KDD. We propose a table that summarizes 14 VDM tools focusing on 3D - VR and interaction techniques based on 3 dimensions: • Visual representations; • Interaction techniques; • Steps in the KDD process. This paper is organized as follows: firstly, we introduce VDM. Then we define the terms related to this field of research. In Section 3, we explain our motivation for using 3D representation and VR techniques. In Section 4, we provide an overview of the current state of research concerning 3D visual representations. In Section 5, we present our motivation for interaction techniques in the context of KDD. In Section 6, we describe the related work about visualization taxonomy and interaction techniques. In Section 7, we propose a new classification for VDM based on both 3D representations and interaction techniques. In addition, we survey representative works on the use of 3D and VR interaction techniques in the context of KDD. Finally, we present possible directions for future research.