From visual data exploration to visual data mining: a survey <s> Visual Data Mining <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> BIB001 </s> From visual data exploration to visual data mining: a survey <s> Visual Data Mining <s> Clustering in high-dimensional databases poses an important problem. However, we can apply a number of different clustering algorithms to high-dimensional data. The authors consider how an advanced clustering algorithm combined with new visualization methods interactively clusters data more effectively. Experiments show these techniques improve the data mining process. <s> BIB002 </s> From visual data exploration to visual data mining: a survey <s> Visual Data Mining <s> Data analysis is fundamentally an iterative process in which you issue a query, receive a response, formulate the next query based on the response, and repeat. You usually don't issue a single, perfectly chosen query and get the information you want from a database; indeed, the purpose of data analysis is to extract unknown information, and in most situations, there is no one perfect query. People naturally start by asking broad, big-picture questions and then continually refine their questions based on feedback and domain knowledge. In the Control (Continuous Output and Navigation Technology with Refinement Online) project at the University of California, Berkeley, the authors are working with collaborators at IBM, Informix, and elsewhere to explore ways to improve human-computer interaction during data analysis. The Control project's goal is to develop interactive, intuitive techniques for analyzing massive data sets. <s> BIB003 </s> From visual data exploration to visual data mining: a survey <s> Visual Data Mining <s> To attack the problem of handling increasingly vast stores of information, we discuss a new approach to data exploration that requires the close coupling of man and machine. We call this approach discovery visualization to emphasize the importance of visual display and interaction. This approach aims to discover new relations, new features, and new knowledge. A key element in discovery visualization lies in heightening the machine's awareness of users so they have, for example, focus-based manipulation, based on where and how closely they look at the displayed scene, in addition to direct manipulation. This process makes no sense unless the machine can respond immediately. Further, we promote the concept of continuous interaction with constant feedback between man and machine, and constant unfolding of the data. Finally, automated response must combine with user selection to achieve and sustain animated action, even in data sets of great or varying complexity. <s> BIB004 </s> From visual data exploration to visual data mining: a survey <s> Visual Data Mining <s> trees have been successfully used for the task of classifi- cation. However, state-of-the-art algorithms do not incorporate the user in the tree construction process. This paper presents a new user-centered approach to this process where the user and the com- puter can both contribute their strengths: the user provides domain knowledge and evaluates intermediate results of the algorithm, the computer automatically creates patterns satisfying user constraints and generates appropriate visualizations of these patterns. In this cooperative approach, domain knowledge of the user can direct the search of the algorithm. Additionally, by providing adequate data and knowledge visualizations, the pattern recognition capabilities of the human can be used to increase the effectivity of decision tree construction. Furthermore, the user gets a deeper understanding of the decision tree than just obtaining it as a result of an algorithm. To achieve the intended level of cooperation, we introduce a new visu- alization of data with categorical and numerical attributes. A novel technique for visualizing decision trees is presented which provides deep insights into the process of decision tree construction. As a key contribution, we integrate a state-of-the-art algorithm for deci- sion tree construction such that many different styles of cooperation - ranging from completely manual over combined to completely automatic classification - are supported. An experimental perfor- mance evaluation demonstrates that our cooperative approach yields an efficient construction of decision trees that have a small size, but a high accuracy. <s> BIB005
Wong BIB001 argues that, rather than using visual data exploration and analytical mining algorithms as separate tools, a stronger DM strategy would be to tightly couple the visualizations and analytical processes into one DM tool. Many mining techniques involve different mathematical steps that require user intervention. Some of these can be quite complex and visualization can support the decision processes involved in making such interventions. From this viewpoint, a Visual Data Mining technique is not just a visualization technique being applied to exploit data in some phases of an analytical mining process, but a DM algorithm in which visualization plays a major role. Although current data exploration systems will certainly have a role to play, future visual DM systems are likely to change to accommodate this new paradigm. A work that illustrates this tight coupling of visualization resources into a mining technique is by Hinneburg et al. BIB002 . They describe an effective approach for clustering high-dimensional data combining an advanced clustering algorithm, called OptiGrid, with visualization methods that support the interactive clustering process. The approach is a recursive one: In each step, the actual data set is partitioned into a number of subsets, if possible, and then the subsets containing at least one cluster are dealt with recursively. The partitioning uses a multidimensional grid defined by a number of separators chosen in regions with minimal point density. The recursion stops for a subset when no good separators can be found. Choosing the contracting projections and specifying the separators for building the multidimensional grid, however, are two difficult problems that cannot be done fully automatically because of the diverse cluster characteristics of different data sets. This is where visualization can help and the authors have developed new techniques that represent the important features of a large number of projections. These techniques help identify the most interesting projections and select the best separators, thus improving the effectiveness of the clustering process and allowing users to find clusters otherwise missed. Hellerstein et al. BIB003 also explore visualization and user interface resources to improve user control over the data discovery process, although the use of visualization and visual widgets is just one aspect of their work. A KDD process comprises several steps and demands considerable user input for issuing queries and/or tuning algorithmspecific parameters, such as support and confidence for association rule mining, thresholds for clustering, training sets for classification, and so on. The authors argue that making the discovery process visible to the user along its steps-and not only at a specific stage-makes it easier to Fig. 11 . Visualization in the CViz system of discovered rules for the UCI Iris flower data set, which consists of 150 data items containing four numeric and one categorical attribute (the flower's class) which may assume three different values. Each discovered rule is represented as a polygonal region. The visualization shows all eight rules learned from the Iris data, including three rules for class 3, three rules for class 2, and one rule for class 1. The user can interact with the visualization to see only the rules on a particular class and/or within certain accuracy and quality thresholds. From  . The counts denote the frequency of the given combination of attribute values in the database. From  . take informed decisions regarding the guidance and termination of the process. For example, the ability of dynamically setting confidence levels for a time-consuming association rule mining algorithm, as it works on the data, would be highly desirable, rather than setting it in the beginning of the process and waiting until it ends to find out that the choice was inadequate. Their work fits into an overall project for investigating mechanisms to improve human-computer interaction during data analysis of massive data sets. In this context, they are investigating user interface widgets for online query formulation and refinement and interactive data visualization algorithms. Their online data visualization technique, named Clouds, works by rendering records as they are fetched from the database, but simultaneously using those records to generate an overlay of shaded regions of colors ("clouds") that estimate the missing data. This way the user can get a feeling of what the overall picture will be and interact with this transient representation, seeing it improve gradually as more records are processed. Ankerst et al. BIB005 ,  also tackle the users inability to intervene on a running DM algorithm or get intermediate results, in the specific context of a classification task. They point out that current classification algorithms provide very limited forms of user guidance and interaction. Users typically select the data set and set some parameter values, which usually are very difficult to determine a priori and then have to wait for the final results. To support better user involvement, their approach to interactive classifier decision tree construction relies heavily on visualization of both the data set and the decision tree. An added benefit of greater user involvement in the decision tree construction is the insight gained into the data. They introduced PBC-Perception Based Classification  , an interactive decision tree classifier that allows users to interact with a multidimensional visualization technique to place split points on numeric attributes for constructing a univariate decision tree. A limitation, however, was that most of the decision tree construction process was carried out manually by the user, who had to select the split attributes and split points. A second version BIB005 brings several improvements. First, both numerical and categorical attributes are supported, thus increasing the range of possible applications. Second, they introduce an improved technique for visualizing the decision trees that provide greater insight into their construction process. They also integrate a decision tree construction algorithm supporting a range of user-computer cooperation levels, ranging from completely manual over combined to completely automatic classification. Fig. 14 shows a screen shot of the PBC prototype system, illustrating a stage of the decision tree construction process. Visualization of the training data is based on a modified Circle Segments technique, with classes mapped to colors. In the version of PBC depicted in the figure, the decision tree is visualized in a standard way that is not related to the data visualization. The later version BIB005 introduces more effective visualizations of both the decision tree and the training data. A similar approach toward classification creates an interactive visual representation of the decision tree to increase user insight on the process  . Both works show concern with comparing the benefits of the visual interactive approach over the automatic ones, having conducted initial experimental evaluations. Ware et al.  concluded that success of the visual approach hinges on the domain and the users' familiarity with the data. Their users could successfully generate accurate classifiers when few attributes could support good predictions, whereas domains with many high-order attributes favored standard machine learning techniques. Ribarsky et al. BIB004 suggest a mining approach which emphasizes user interaction, calling it "discovery visualization." Such an approach differs from DM in that it centers on the users, with responsiveness matched to maximize their capabilities. In particular, it relies heavily on 4D (timedependent) visual display and interaction, which requires close attention to the organization of data for both graphical representation and fast, accurate selection via the visualization. They describe a fast clustering algorithm that fits into this approach, supporting data exploration by continuous adjustment and feedback via interaction with the visualization. Their algorithm provides fast clustering, is scalable to very large data sets, and extends beyond direct spatial clustering to the distribution of other variables. It uses an initial binsort to scale the data to a more manageable size and initially considers the entire (binsorted) data space as one big cluster. Then, the data set is iteratively subdivided until a user-specified number of clusters are found or until it makes no sense to subdivide any further. This approach allows displaying a general overview of the distribution of the data very quickly from which the user can select regions of interest for further exploration. It is illustrated with two applications involving large collections of data. The authors argue that a complete visual DM approach needs a framework to support a highly interactive exploration and discovery process for data of any scale, in addition to supporting fast queries and collection of data. They designed a hierarchical paging mechanism for visual DM, which supports rapid display and provides the data in the appropriate context. The paging structure was built as a height-balanced feature tree in which the clusters define the features. The hierarchy permits navigable visualizations where users can zoom in, see detail in context, or back up to gain an overview. Only the top structure of the tree and those sections being currently explored reside in the main memory, thus ensuring scalability to large amounts of data. A "skeleton tree" is kept in memory with a sufficient set of linking properties so that the next section of the tree or associated data can be retrieved, based on the user controlled visual exploration process.