WebRuleVis

This module provides a visual representation of the association rules encapsulated in the input Rule Table. Detailed Description: This module presents a visual representation of association rules identified by a discovery algorithm. Seasr includes several modules that implement association rule discovery algorithms, all of which save their results in a Rule Table structure that can be used as input to this module. The main region of the display contains a matrix that visually depicts the rules. Each numbered column in the matrix corresponds to an association rule that met the minimum support and confidence requirements specified by the user in the rule discovery modules. Items used in the rules, that is attribute-value pairs, are listed along the left side of the matrix. Note that some items in the original data set may not be included in any rule because there was insufficient support and/or confidence to consider the item significant. An icon in the matrix cell corresponding to ( row = item i, column = rule r) indicates that item i is included in rule r. If the matrix cell icon is a box, then the item is part of the rule antecedent. If the icon is a check mark, then the item is part of the rule consequent. For example, if the rules being displayed indicate whether or not a mushroom is edible, a rule might be odor=none and ring_number=one then edibility=edible. This rule would be displayed in a column with a box in the row for the item odor=none and a box in the row for ring_number=one, and there would be a check in the row for edibility=edible. Above the main matrix are two rows of bars labeled Confidence and Support. These bars align with the corresponding rule columns in the main matrix. For any given rule, the confidence and support values for that rule are represented by the degree to which the bars above the rule column are filled in. Brushing the mouse on a confidence or support bar displays the exact value that is graphically represented by the bar height. The rules can be ordered by confidence or by support. To sort the rules, click either the support or the confidence label -- these labels are clickable radio buttons. If support is selected, rules will be sorted using support as the primary key and confidence as the secondary key. Conversely, if the confidence button is chosen, confidence is the primary sort key and support is the secondary key. Directly above the confidence and support display is a toolbar that provides additional functionality. On the left side of the toolbar are two buttons that allow the rows of the table to be displayed according to different sorting schemes. One of the buttons is active at all times. The Alphabetize button sorts the attribute-value pairs alphabetically. The Rank button sorts the rows based on the current Confidence/Support selection, moving the consequents and antecedents of the highest ranking rules to the top of the attribute-value list. On the right side of the toolbar are four additional buttons: Restore Original reverts back to the original table that was displayed before any sorting was done. Filter provides an interface that allows the user to display a subset of the generated rules. The user can scroll to different part of the matrix to get the full picture of a large matrix. Help displays information describing the visualization. Scalability: While the visualization can display a large number of items and rules, there can be a noticeable delay in opening the visualization when a large number of cells are involved. Also, as the number of cells increases beyond a certain point, it is difficult to gain insights from the display. Advanced features to help in these cases are being discussed for a future release.