From visual data exploration to visual data mining: a survey <s> ONGOING RESEARCH ON VDM <s> Knowledge discovery in databases is a rapidly growing field, whose development is driven by strong research interests as well as urgent practical, social, and economical needs. While the last few years knowledge discovery tools have been used mainly in research environments, sophisticated software products are now rapidly emerging. In this paper, we provide an overview of common knowledge discovery tasks and approaches to solve these tasks. We propose a feature classification scheme that can be used to study knowledge and data mining software. This scheme is based on the software's general characteristics, database connectivity, and data mining characteristics. We then apply our feature classification scheme to investigate 43 software products, which are either research prototypes or commercially available. Finally, we specify features that we consider important for knowledge discovery software to possess in order to accommodate its users effectively, as well as issues that are either not addressed or insufficiently solved yet. <s> BIB001 </s> From visual data exploration to visual data mining: a survey <s> ONGOING RESEARCH ON VDM <s> Method and system for finding targets within visual data, comprising receiving target object information. Generating a set of target object semantic attributes from the target object information. Identifying a plurality of candidate objects within visual data. Generating a set of low-level feature descriptors from the visual data for each candidate object. Generating from the set of low-level feature descriptors a set of candidate semantic attributes for each candidate object within the visual data. Identifying one or more portions of the visual data containing a candidate object, from the plurality of candidate objects, having a set of candidate object semantic attributes that match the set of target object semantic attributes. Providing an output indicating the identified one or more portions of the visual data. <s> BIB002
Much of the commercial software marketed as visual DM systems is highly interactive visualization systems targeted at data exploration, with varying degrees of support for data preprocessing, external database connection, and, eventually, specific DM algorithms. A major problem that hampers the use of visual data exploration techniques in DM is that many of the well-established visual techniques do not scale well with respect to data set size. Goebel and Grunenwald BIB001 have surveyed off-the-shelf commercial and academic DM/KDD tools and, from a total of 42 products reviewed, 23 are said to include resources for visually conveying the results of an analysis (model visualization) and 10 products out of these also include facilities for exploratory data analysis. So, albeit visualization does play a major role in supporting DM, it is not yet widely integrated into commercial software. Most data analysts use visualization as part of a process sandwich strategy of interleaving mining and visualization to reach a goal, an approach commonly identified in many research works on applications and techniques for visual DM. As pointed out by Wong BIB002 , usually the analytical mining techniques themselves do not rely on visualization. Most of the papers describing visual DM approaches and applications found in the literature fall into two categories. Either they use visual data exploration systems or techniques to support a knowledge extraction goal or a specific mining task or they use visualization to display the results of a mining algorithm, such as a clustering process or a classifier, and thus enhance user comprehension of the results. Examples from the first group are discussed in Section 4.1 and those from the second group are described in Section 4.2. A more promising approach, however, is to create visual representations of models created along the steps of an analytical mining algorithm (or, more broadly, along the steps of the whole knowledge extraction process), with the goal of supporting users in the process of interacting with the algorithm. Examples that are, to different extents, illustrative of such an approach are described in Section 4.3.