Data Mining_ Concepts and Techniques - Jiawei Han [363]
Figure 13.4 Boxplots showing multiple variable combinations in StatSoft. Source:www.statsoft.com.
Figure 13.5 Multidimensional data distribution analysis in StatSoft. Source:www.statsoft.com.
■ 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 (Chapter 2), as well as decision trees, association rules, clusters, outliers, and generalized rules. For example, scatter plots are shown in Figure 13.6 from SAS Enterprise Miner. Figure 13.7, from MineSet, uses a plane associated with a set of pillars to describe a set of association rules mined from a database. Figure 13.8, also from MineSet, presents a decision tree. Figure 13.9, from IBM Intelligent Miner, presents a set of clusters and the properties associated with them.
Figure 13.6 Visualization of data mining results in SAS Enterprise Miner.
Figure 13.7 Visualization of association rules in MineSet.
Figure 13.8 Visualization of a decision tree in MineSet.
Figure 13.9 Visualization of cluster groupings in IBM Intelligent Miner.
■ 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 13.10 shows a visual presentation of data mining processes by the Clementine data mining system.
Figure 13.10 Visualization of data mining processes by Clementine.
■ 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 13.11, which is the output of a perception-based classification (PBC) system developed at the University of Munich.
Figure 13.11 Perception-based classification, an interactive visual mining approach.
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 pitchs, rhythm, tune, and melody 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.
13.3. Data Mining Applications
In this book, we have studied principles and methods for mining relational data, data warehouses, and complex data types. Because data mining is a relatively young discipline with wide and diverse applications, there is still a nontrivial gap between general principles of data mining and application-specific, effective data mining