Introduction: Visual data mining discovers implicit and useful knowledge from large data sets using data and/or knowledge visualization techniques. The human visual system is controlled by the eyes and brain, the latter of which can be thought of as a powerful, highly parallel processing and reasoning engine containing a large knowledge base. Visual data mining essentially combines the power of these components, making it a highly attractive and effective tool for the comprehension of data distributions, patterns, clusters, and outliers in data.
Visual data mining can be viewed as an integration of two disciplines: data visualization and data mining. It is also closely related to computer graphics, multimedia systems, human computer interaction, pattern recognition, and high-performance computing. In general, data visualization and data mining can be integrated in the following ways:
Data visualization: Data in a database or data warehouse can be viewed at different levels of granularity or abstraction, or as different combinations of attributes or dimensions. Data can be presented in various visual forms, such as boxplots, 3-D cubes, data distribution charts, curves, surfaces, link graphs, and so on. Figures 11.2 and 11.3 from Stat Soft show data distributions in multidimensional space. Visual display can help give users a clear impression and overview of the data characteristics in a database.
Data mining result visualization: Visualization of data mining results is the presentation of the results or knowledge obtained from data mining in visual forms. Such forms may include scatter plots and boxplots (obtained from descriptive data mining), as well as decision trees, association rules, clusters, outliers, generalized rules, and so on. For example, scatter plots are shown in Figure 11.4 from SAS Enterprise Miner. Figure 11.5, from Mine Set, uses a plane associated with a set of pillars to describe a set of association rules mined from a database. Figure 11.6, also from MineSet, presents a decision tree. Figure 11.7, from IBM Intelligent Miner, presents a set of clusters and the properties associated with them.
Data mining process visualization: This type of visualization presents the various processes of data mining in visual forms so that users can see how the data are extracted and from which database or data warehouse they are extracted, as well as how the selected data are cleaned, integrated, preprocessed, and mined. Moreover, it may also show which method is selected for data mining, where the results are stored, and how they may be viewed. Figure 11.8 shows a visual presentation of data mining processes by the Clementine data mining system.
Interactive visual data mining: In (interactive) visual data mining, visualization tools can be used in the data mining process to help users make smart data mining decisions. For example, the data distribution in a set of attributes can be displayed using colored sectors (where the whole space is represented by a circle). This display helps users determine which sector should first be selected for classification and where a good split point for this sector may be. An example of this is shown in Figure 11.9, which is the output of a perception-based classification system (PBC) developed at the University of Munich.
Audio data mining uses audio signals to indicate the patterns of data or the features of data mining results. Although visual data mining may disclose interesting patterns using graphical displays, it requires users to concentrate on watching patterns and identifying interesting or novel features within them. This can sometimes be quite tiresome. If patterns can be transformed into sound and music, then instead of watching pictures, we can listen to pitches, rhythms, tune, and melody in order to identify anything interesting or unusual. This may relieve some of the burden of visual concentration and be more relaxing than visual mining. Therefore, audio data mining is an interesting complement to visual mining.
