Data mining concerns the discovery and extraction of knowledge chunks from large data repositories. In a cooperative datamining environment, more than one data mining tool collaborates during the knowledge discovery process. This chapter describes a data mining approach used to visualize the cooperative data mining process. According to this approach, visual data mining consists of both data and knowledge visualization. First, the data are visualized during both data preprocessing and data mining. In this way, the quality of the data is assessed and improved throughout the knowledge discovery process. Second, the knowledge, as discovered by the individual learners, is assessed and modified through the interactive visualization of the cooperative data mining process and its results. The knowledge obtained from the human domain expert also forms part of the process. Finally, the use of virtual reality-based visualization is proposed as a new method to model both the data and its descriptors.
The current explosion of data and information, mainly caused by the extensive use of the Internet and its related technologies, e-commerce and e-business, has increased the urgent need for the development of techniques for intelligent data analysis. Data mining, which concerns the discovery and extraction of knowledge chunks from large data repositories, is aimed at addressing this need.
However, there are a number of factors that militate against the widespread adoption and use of this existing new technology in business. First, individual data mining tools frequently fail to discover large portions of the knowledge embedded in large data repositories. This is mainly due to the choice of statistical measures used by the individual tools. A number of data mining researchers and practitioners are, therefore, currently investigating systems that combine two or more diverse data mining tools. In particular, the combination of techniques that share their individual knowledge with one another is being investigated, leading to the fusion of information representing different viewpoints.
Second, the results of many data mining techniques are often difficult to understand. For example, a data mining effort concerning the evaluation of a census data repository produced 270 pages of rules (Pretorius, 2001). The visual representation of the knowledge embedded in such rules will help to heighten the comprehensibility of the results. The visualization of the data itself, as well as the data mining process, should go a long way towards increasing the user's understanding of and faith in the data mining process. That is, data and information visualization provides users with the ability to obtain new insights into the knowledge, as discovered from large repositories. Human beings look for novel features, patterns, trends, outliers and relationships in data (Han & Kamber, 2001). Through visualizing the data and the concept descriptions obtained (e.g., in the form of rules), a qualitative overview of large and complex data sets can be obtained. In addition, data and rule visualization can assist in identifying regions of interest and appropriate parameters for more focused quantitative analysis (Thearling, Becker, DeCoste, Mawby, Pilote & Sommerfield,2002). The user can thus get a "rough feeling" of the quality of the data, in terms of its correctness, adequacy, completeness, relevance, etc. The use of data and rule visualization thus greatly expands the range of models that can be understood by the user, thereby easing the so-called "accuracy versus understandability" tradeoff (Thearling et al., 1998).
Visual data mining is currently an active area of research. Examples of related commercial data mining packages include the DBMiner data mining system, See5 which forms part of the RuleQuest suite of data mining tools, Clementine developed by Integral Solutions Ltd (ISL), Enterprise Miner developed by SAS Institute, Intelligent Miner as produced by IBM, and various other tools (Han & Kamber, 2001). Neural network tools such as NeuroSolutions and SNNS and Bayesian network tools such as Hugin, TETRAD, and Bayesware Discoverer, also incorporate extensive visualization facilities. Examples of related research projects and visualization approaches include MLC++, WEKA, AlgorithmMatrix, C4.5/See5 and CN2, amongst others (Clark & Niblett, 1989; Fayyad, Grinstein, & Wierse, 2001; Han & Kamber, 2001; Mitchell, 1997; Quinlan, 1994) Interested readers are referred to Fayyad, Grinstein, & Wierse (2001), which provides a detailed discussion of the current state of the art.
This paper describes the ViziMine data mining tool used to visualize the cooperative data mining process. The aim of the ViziMine tool is twofold: First, the knowledge, as discovered by the individual tools, is visualized throughout the data mining process and presented in the form of comprehensible rules; this aspect includes visualization of the results of data mining as well as the cooperative learning process. Second, the data are visualized prior to and during cooperative data mining. In this way, the quality of the data can be assessed throughout the knowledge discovery process, which includes data preprocessing, data mining, and reporting. During data mining, the visualization of data as covered by individual rules shows the portion of the data covered. The visual data mining process is interactive in that humans are able to adapt the results and data during mining. In addition, this chapter shows how virtual reality can be used to visualize the data and its descriptors.
The chapter is organized as follows: The next section introduces the cooperative inductive learning team (CILT) data mining system in which two or more data mining tools co-exist. An overview of current trends in visual data mining follows. The next section discusses the ViziMine system, which incorporates visual data mining components into the CILT system. Then we introduce the use of three-dimensional visualization, virtual reality-based visualization and multimedia data mining, which may be used to visualize the data used for data mining. Conclusions are presented in the last section of the chapter.