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 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.

Automatic Binning groups together closely spaced numerical data into discrete categories. Some data mining algorithms, such as the Decision Tree Inducer, require some discrete (categorical) data; similarly, visualization tools such as the Splat Visualizer may need data categorized in this way.

MineSet can automatically determine these categories, or you can determine how you need it done. Requirements can be as simple as dividing the data into three equal ranges; or as complex as having MineSet choose ranges differentiated according to some chosen attribute, at the same time discarding the outer five percent of the data as outliers.

Clustering segments data into similar groups or clusters. For example, you can ask MineSet to suggest a segmentation of customers into five distinct groups, without giving any further parameters. Once the clustering operation has been run, you can view the results in the Cluster Visualizer; or apply the clustering model to the current data, then analyze the resulting clusters in any MineSet visualization or mining tool.

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 can then be used to configure various visualizers.

Column Importance has an advanced mode that provides additional capabilities. First, it lets you determine how important each of the attributes is. (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, you might 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 Decision Table Classifier classifies data by making a series of consecutive decisions leading to the classification based on a record's attributes. It can be used to predict events such as whether a bank customer is likely to default on a loan, or a homeowner is likely to refinance their mortgage.

The Decision Table Inducer creates a Decision Table Classifier from the data. Attributes are tested to classify the data, and you have the option to set the order in which the tests are run as well. The resulting Decision Table Classifier can be viewed using the Decision Table Visualizer, so you can simultaneously explore multiple attribute tests, two at a time.

The Decision Tree Classifier classifies data according to a set of attributes by making a series of decisions based on those attributes. 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, the structure of which is displayed using the Tree Visualizer, each decision being represented by a node of the tree. The graphical representation helps you understand the model, as well as gives valuable insight into the data, by using visual searching and filtering.

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 model 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.

The Option Tree Classifier classifies data using a technique similar to the Decision Tree Classifier. Unlike decision trees, option trees can contain special option nodes, which allow the classifier to consider the influence of splitting on multiple attributes simultaneously. For example, an option node in an option tree built to identify a car's country of origin might choose miles per gallon, horsepower, number of cylinders, and weight as informative attributes. In a decision tree, a node can choose at most one attribute for consideration at a time. In an option tree, the results of all options are “voted” when performing classification. Option trees are often more accurate than decision trees; however, they generally are much larger.

The Option Tree Inducer generates an Option Tree Classifier from a training set in much the same way that the Decision Tree inducer generates a Decision Tree. The induced option tree is displayed using the Tree Visualizer. This visualization helps you understand the classifier, and provides insight into which attributes are important in determining the value of the label.

The Regression Tree Regressor predicts continuous attributes, in the same the way that the Decision Tree and Option Tree Classifiers predict discrete attributes. While a classifier predicts an event, such as whether a customer will churn (leave you) or not, a regressor predicts specific numerical values, such as the profit margin for a business for the next financial quarter.

The Regression Tree Inducer builds a Regression Tree Regressor model from your data. As with Decision and Option Trees, this model can be viewed and analyzed using the Tree Visualizer, so you can understand the basis from which its predictions are made.

The Cluster Visualizer displays statistics about the clusters or groups that are generated by the clustering mining tool. It places these statistics side-by-side with those for the entire data set, so that you can see which features make each cluster unique.

The Cluster Visualizer places the attributes in the display in the order of importance for understanding the clustering. When you select one particular cluster, Cluster Visualizer produces an ordering which is the most useful for discriminating between that cluster and the remainder of the data set.

The Decision Table Visualizer allows you to view the distribution of data from a discrete column at multiple levels of a hierarchy. For example, you can examine the profitability of a business along dimensions of product class, geography, sales promotions and sales-representative compensation plan. The Decision Table Visualizer distributes the data two attributes at a time, allowing you to drill-down to further pairs of attributes at each level.

The Decision Table Visualizer explores the results of the Decision Table Inducer, so that the discrete column you examine is the label that the inducer classifies. When this is done, the Decision Table Inducer arranges the attributes to determine which pair to display first, and how to drill down from that top level to subsequent levels.

The Evidence Visualizer visually represents the model generated by the Evidence Classifier. It initially shows cake charts that represent how the various attributes contribute to the decision, and allow “what-if” analysis.

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 Record Viewer lets you view the data in the current table in a row/column spreadsheet-like tool.

The Rules Visualizer visually represents the model 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 future.

The Scatter Visualizer lets you examine the behavior of data across eight 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 used 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 variables.

The Splat Visualizer produces 3D plots of very large data sets. Instead of showing individual data points, it renders the density of data using varying opacity. It has many of the same features as the Scatter Visualizer.

The Statistics Visualizer computes and displays summary information for the current dataset (maximum, minimum, median, standard deviation, distinct values, and quartiles).

The Tree Visualizer helps you analyze data that has hierarchical relationships. It provides an interactive “fly-through” capability for examining relationships among 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 resulting models of the Decision Tree and Option Tree Classifiers, and the Regression Tree Regressor; with each decision being represented by a separate node in the tree. Each node also contains bars showing how the data is modeled 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).