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problems of unstructured visualization of documents and unstructured data. With a properly constructed SOM, you can analyze literally millions of unstructured documents that can be merged into a single SOM. The SOM deals not only with individual unstructured documents but relationships between documents as well. The SOM may show text that is correlated to other text. For example, in the medical field, working with medical patient records, this ability to correlate is very attractive. The SOM also allows the analyst to see the larger picture as well as drilling down to the detailed picture. The SOM goes down to the individual stemmed-text level, and that is as accurate as textual processing can become. All these characteristics have resulted in the growing popularity of SOM visualizations in order to assist visual inspection of complex high-dimensional data. For the end user the flexibility of the SOM algorithm is defined through a number of parameters. For appropriate configuration of the network, and tuning the visualization output, user-defined parameters include grid dimensions (2-D, 3-D), grid shape (rectangle, hexagon), number of output nodes, neighborhood function, heighborhood size, learning-rate function, initial weights in the network, way of learning and number of iterations, and order of input samples.

15.6 VISUALIZATION SYSTEMS FOR DATA MINING

Many organizations, particularly within the business community, have made significant investments in collecting, storing, and converting business information into results that can be used. Unfortunately, typical implementations of business “intelligence software” have proven to be too complex for most users except for their core reporting and charting capabilities. Users’ demands for multidimensional analysis, finer data granularity, and multiple-data sources, simultaneously, all at Internet speed, require too much specialist intervention for broad utilization. The result is a report explosion in which literally hundreds of predefined reports are generated and pushed throughout the organization. Every report produces another. Presentations get more complex. Data are exploding. The best opportunities and the most important decisions are often the hardest to see. This is in direct conflict with the needs of frontline decision makers and knowledge workers who are demanding to be included in the analytical process.

Presenting information visually, in an environment that encourages the exploration of linked events, leads to deeper insights and more results that can be acted upon. Over the past decade, research on information visualization has focused on developing specific visualization techniques. An essential task for the next period is to integrate these techniques into a larger system that supports work with information in an interactive way, through the three basic components: foraging the data, thinking about data, and acting on data.

The vision of a visual data-mining system stems from the following principles: simplicity, visibility, user autonomy, reliability, reusability, availability, and security. A visual data-mining system must be syntactically simple to be useful. Simple does not mean trivial or non-powerful. Simple to learn means use of intuitive and friendly input mechanisms as well as instinctive and easy-to-interpret output knowledge. Simple to apply means an effective discourse between humans and information. Simple to retrieve or recall means a customized data structure that facilitates fast and reliable searches. Simple to execute means a minimum number of steps needed to achieve the results. In short, simple means the smallest, functionally sufficient system possible.

A genuinely visual data-mining system must not impose knowledge on its users, but instead guide them through the mining process to draw conclusions. Users should study the visual abstractions and gain insight instead of accepting an automated decision. A key capability in visual analysis, called visibility, is the ability to focus on particular regions of interest. There are two