Each of the mining and visualization tools described below can be configured and started via a consistent graphical user interface known as the Tool Manager. The Tool Manager

The DataMover is a process that runs on the server on behalf of the user. The DataMover

The Association Rules Generator part of this tool processes an input file, then generates an output file consisting of rules. These rules indicate the frequency with which one item occurs in a record along with another item. The strength of the association is quantified by three numbers.

The Decision Tree Classifier classifies data according to a set of attributes by making a series of decisions based on those attributes. The process is similar to using a biological key to identify plants. Applying this classifier to determine the profile of someone with credit worthiness, for example, a decision tree might determine if someone who owns a home, owns a car that cost between $15,000 and $23,000, and has two children, is a good credit risk.

The Decision Tree Inducer generates a decision tree classifier from a “training set” (a set of data that the user has already classified). Then, the structure of the classifier's decision tree is displayed using the Tree Visualizer, with each decision being represented by a node of the tree. The graphical representation can help the user understand the classification algorithm, as well as provide valuable insights into the data. Finally, the classifier can be used to classify unclassified data.

The Evidence Classifier classifies data by examining the probabilities of a specified result occurring based on a given attribute. For example, it might determine that someone who owns a car that cost between $15,000 and $23,000 has a 70% chance of being a good credit risk, and a 30% chance of being a bad credit risk. The classifier predicts the class with the highest probability based on a simple probabilistic model.

The classifier is first generated from a training set, similar to the decision tree classifier. The analysis of the data is displayed using the Evidence Visualizer, which shows pie charts illustrating the different probabilities. This graphical representation can help the user understand the classification algorithm, as well as providing valuable insights into the data and answering “what if” questions. Finally, the classifier can be used to classify unclassified data.

Column Importance determines how important various attributes are for determining the value of a given label attribute. For example, you can ask MineSet to select automatically the best three attributes that help determine whether someone is a good credit risk. The system might select income, own-house, and car-cost. These attributes then can be mapped to the axes of the Scatter Visualizer, or used in the hierarchy of the Tree Visualizer.

Column Importance has an advanced mode that provides additional capabilities. First, it lets you determine how important each of the attributes are. (For example, you could determine that both income and salary are similar in importance in determining credit risk. Although income might be slightly better in determining importance, perhaps you would prefer to use salary because it is easier to obtain.) Second, once you explicitly choose an attribute, you can determine what other attributes are important in conjunction with it. (For example, if you have chosen salary rather than income, house-cost might become more important than own-house, and income would have a very low importance.)

The Tree Visualizer helps you analyze data that has hierarchical relationships. It provides an interactive “fly-through” capability for examining the relations between data at different hierarchical levels. For example, the Tree Visualizer can be used to examine a company's product line, graphically displaying each product's contribution to the company's total revenue. Each branch of the hierarchy displays information at increasing levels of detail, breaking revenues down by product lines and, eventually, individual products. Another example of using the Tree Visualizer is to show company sales revenue, displaying a company-wide total as well as sub-totals at regional and other levels. The fly-through capability in the Tree Visualizer lets you rapidly reposition your view of the data. The Tree Visualizer's filtering and searching capabilities let you focus on specific data elements and queries.

The Tree Visualizer is also used to view the results of the Decision Tree Classifier, with each decision being represented by a separate node in the tree. Each node also shows bars showing how the classifier classifies the data based on the decisions up to that point (for example, 73% of people who own a home and have two children are good credit risks, while 27% are not).

The Map Visualizer lets you visualize data relationships that exist across geographically meaningful areas. For example, you can visualize different areas of a country, showing the relative impact of a marketing program. The Map Visualizer's drill-down capabilities let you focus on designated regions and perform a more detailed analysis in smaller geographical elements. One application might be analyzing how one or more products are being sold across different geographies. A powerful animation feature, coupled with a capability to connect different views of the same or related data, permits fast comparisons and difference analyses. This tool lets you visually examine patterns in your data that are difficult to detect when that data is shown in a tabular, two-dimensional form.

The Scatter Visualizer lets you examine the behavior of data across different dimensions. The data is shown in a grid representing up to three dimensions. Extra dimensions can map to the size, color, and label of each displayed entity. Two further independent dimensions can be assigned as dynamic dimensions. A slider can be use to select specific values along those dimensions, or a path can be traced through those dimensions, for animation. During the path traversal, the display changes automatically to reflect the change in the independent variable.

The Rules Visualizer visually represents the results of the Association Rules Generator mining tool. It provides detailed data analysis that lets you examine relationships across data elements in new ways. In doing so, you might discover relationships that significantly differ from what you might have expected; this, in turn, can lead to important discoveries about your data or the processes behind that data. This tool's visualization capabilities let you discover additional patterns of co-occurrence between these data elements. For example, you can use the analysis of products sold during the last sales promotion to guide your advertising campaign for the next sales period. The Rules Visualizer's high performance would let you analyze the results from today's sales data in time to alter the advertising campaign for the following day.

The Evidence Visualizer visually represents the results of the Evidence Classifier. It initially shows pie charts that represent how the various attributes contribute to the decision. For example, it might show that owning a home contributes to being a good credit risk. By clicking on the pie charts, one can show the effect of combining various attributes has on the final result; for example: what happens in a household that rents, has one child, and drives a car valued between $8,000 and $12,000.