This chapter discusses the functions of the Tool Manager, which is the graphical user interface (GUI) that lets you specify data and configuration information for the MineSet tools in this package. It provides an overview of this interface, then describes every component of each panel that this interface displays for all MineSet tools.
![]() | Note: Any screens dedicated to a specific tool are discussed in the chapter for that tool; for example, the screen for specifying the Tree Visualizer's configuration file is discussed in Chapter 4, “Using the Tree Visualizer.” |
The Tool Manager provides the initial GUI for most of your interactions with the MineSet tools. This GUI lets you start the individual tools and specify the following:
the data you want to analyze
from a database
from a file
the set of transformations used to get from the data you are capturing to the data that is displayed:
mining tools—finding patterns in data
binning variables—discretizing column values into groups, such as grouping years by decade
making arrays—taking the values of one column and turning them into an array indexed by discrete values in another column
distributing columns—making two or more new columns from a single column of values, distributed by the discrete values of another column
removing columns—excising unneeded columns to save space
adding new columns—functions of old columns
how you want the data displayed on the screen; for instance,
as a hierarchy (Tree Visualizer)
as a map (Map Visualizer)
as relations of numerous independent variables (Scatter Visualizer)
as associated rules (Rules Visualizer)
as evidence (Evidence Visualizer)
specific mappings of data values to visual elements on the screen, such as colors, bars, heights, and so forth
non-data-related tool options, including
background colors
grid spacing
label sizes
![]() | Note: The Tool Manager generally does not support data files not created by the Tool Manager (without some manual work to make them compatible). |
You can run the Tool Manager in two modes:
interactive mode—the Tool Manager provides windows, menus, buttons, and so on, to let you access, mine, and visualize your data. Interactive mode also lets you save a description of your actions to a “session file” for future use.
batch mode—the Tool Manager performs all the actions described in the session file without bringing up windows. For example, batch mode is useful for lengthy computations that need to be done every night, so that the data can be fully prepared each morning.
There are three ways to start the Tool Manager in interactive mode:
Double-click the MineSet icon, which is in the Applications or the MineSet page of the icon catalog. The Tool Manager starts with the same configuration used in the last Tool Manager session.
Double-click an icon representing a configuration saved from a previous invocation of the Tool Manager. This starts the Tool Manager with that configuration file.
Start the Tool Manager from the UNIX shell command line by entering this command at the prompt:
mineset [ configFile ] |
Here, configFile is optional and specifies the name of the configuration file to use. If you do not specify a configuration file, MineSet starts up with the configuration most recently used.
To start the Tool Manager in batch mode, enter this command at the UNIX shell prompt:
mineset_batch [-s serverPassword -d databasePassword] configFile |
The -s and -d options allow you to specify the password for logging into the server and database respectively. If you do not specify these options, mineset_batch will ask you to type in the passwords, thus these options are useful when running mineset_batch from a shell script. To specify that there is no password for either the server or database, use -s or -d followed by two double quotes, that is,
mineset_batch -s "" -d "" foo.mineset |
If you specify one of the two passwords, you must specify both.
Figure 3-1 shows the Tool Manager's startup window.
This window consists of three panels:
The Server Name and Data Source panel provides a starting point for working with data. Server Name lets you specify the server where database and mining operations are to take place. Data Source establishes where the data comes from and what type it is.
Data Transformations allows you to modify the data from your data source.
Data Destination lets you create visualizations based on your data, save the data to a file, or mine the data for association rules, create classifiers based on the data, or find important columns in the data.
![]() | Note: The latter screens are explained in the respective tool's chapter. |
The following sections describe each panel of the main Tool Manager window.
The Server Name and Data Source panel (Figure 3-2) lets you specify the name of the server on the network to which you want to connect and choose the data source.
At the top of the Data Source panel are two tabs:
Database
Data File
Click one of these, depending on whether you want to work with a database or a data file.
The first time you use the program, the Server Name text box is blank. Type in the name of the server you want to access. After this, the name of the last server accessed appears in the box by default.
You must connect to a server to get information from a database or mining tool, or to apply transformations to an existing data file. It is not necessary if you plan to use an existing client data file without transforming it.
The Log in to Server button is the equivalent of pressing the Enter key on the keyboard when the cursor is in the Server Name box. After you have entered a server name in the text box, clicking Log in to Server (or pressing the Enter key) causes the Tool Manager to connect to that server.
When you have specified a server and clicked Log in to Server (or pressed the Enter key), a dialog box prompts you to type in the login name and password of your account on that server. When these are accepted, your workstation is connected to the server.
If you want to work with a data file previously created by the Tool Manager, select Data File from the tabbed list. The buttons in the Data Source panel change (see Figure 3-4), letting you specify the name of a .schema file (which describes some data file). To specify the name, choose either Client or Server.
If you choose Client, click Find Client File... to bring up a file selection dialog (Figure 3-3).
If you choose Server, the menu to the right of the server toggle becomes active, letting you select a server file from a popup menu (Figure 3-4).
If you want to access a data file created outside of Tool Manager, you must create a .schema file for it. This is a text file containing a configuration file “input” section, which gives the name of the data file and describes its layout. For a description of the input section, see the appendix for the tool you want to use.
If you clicked the Database tab, the DBMS button becomes active after you've logged in to the server. Before you choose a database, the DBMS button says None-Selected. When you click it, a menu of the database managers appears. Choose the one you want by clicking it. At this point, a dialog window pops up and asks you for your DBMS login name and password before the program grants access to the database. (Note that the login name and password for the DBMS are probably different from those for logging into the server.) After you enter them, the rest of the Data Source buttons become active.
The Tool Manager lets you connect to an Oracle, INFORMIX, or Sybase database. If you choose an Oracle database, the Database button remains inactive (grayed out) because an Oracle database has only one set of tables per database manager. If, however, you choose an INFORMIX or Sybase manager, the Database button becomes active because each of these managers can have several sub-databases. Click it for a menu of the databases, and choose the one you want to work with.
The Table Type popup menu lets you specify whether your data source is a table or view from the database (Single Table) or an SQL query (SQL Query).
To run an SQL query, click the Table Type button and choose SQL Query. Type your query statement in the window that appears under Table (see the example in Figure 3-6).
Instead of typing an SQL query, you can load a previously written query from a file by clicking the Load SQL button. A file selection dialog box appears so you can choose the file containing the SQL query.
After entering the query statement, press Submit SQL. If the query is valid, the schema for the table resulting from the query appears in the Data Transformations panel. If there is an error in the query, a dialog box appears, explaining the error (Figure 3-7).
To work with all the data in a database table, select Single Table from the Table Type menu. If you want to work with data in multiple tables or a subset of data in one or more tables, run an SQL query to find the information with which you want to work.
When you change servers, the Data Source panel updates its list of tables to reflect those tables found on the new server.
The Data Transformations panel lets you manipulate the tables with which you want to work. After you have selected a table (via the Server Name and Data Source panel described above), its column headings appear in the Current Columns window of the Data Transformations panel (Figure 3-8).
The Data Transformation data manipulation options are as follows:
Remove Columns—lets you delete one or more columns that are not relevant to the current visualization or mining.
Bin Columns—lets you take a range of values and assign each record to a group (for example, with a range of ages, 0-18, 19-25, 26-35, and so on).
Aggregate—lets you find aggregations (sum, min, max, and so on), group data into new columns, or make arrays from a column indexed by other columns.
Change Types—lets you change a column's type.
Add Column—lets you add a new column based on a mathematical expression.
Apply Classifier—lets you use a previously created classifier (see Chapter 9 and Chapter 10) on a table for labeling new records.
Remove Column lets you delete columns by selecting the column name or names in the Current Columns panel, then clicking this button. The items in the Current Columns panel change to show the new table columns. To choose multiple contiguous columns for simultaneous removal, drag the mouse over the columns. To choose multiple non-contiguous columns for simultaneous removal, hold down the Ctrl key while selecting the additional columns.
Binning lets you sort the information from one or more columns into groups in a new column or columns (for example, with a range of ages, 0-18, 19-25, 26-35, and so on). Click Bin Column to get a dialog box that lets you specify the binning options (Figure 3-9).
This dialog box lets you
choose the column that is to be divided into bins
specify the name of the new column to contain values for the bins
set bin thresholds, or specify a range with thresholds at regular intervals
To specify binning options for one or more columns, select the column name(s), choose the appropriate options below, and click the Apply button at the bottom of the dialog box.
If you select only one column for binning, the name of the resulting binned column appears in the New column name box, and you can type in a new name if you like. In the example shown in Figure 3-9, Age_Bin is the name for the new column; in this case, it provides a range of ages. If you select more than one column for binning, New column name stays inactive.
Next to New column name is a check box labeled Delete original column. When chosen, this option automatically deletes the original column after binning. Click the check box to turn this function on or off.
In the middle of the Bin columns dialogue box are two tabs for choosing Automatic Thresholds or User Specified Thresholds. Choose Automatic Thresholds if you'd like the computer to suggest the bins or User Specified Thresholds if you'd like to specify the thresholds yourself.
If you've chosen the Automatic Thresholds tab, the program can use machine learning to suggest bins.
The first choice under Automatic Threshold Computation is between the Automatically choose number of bins and the Group into: ___ bins buttons. Click Automatically choose number of bins to let the computer decide the best number of bins. If you choose to specify the number of bins, click Group into: ___ bins, and type the number of bins you want into the field.
In the Use approach menu, you can choose between Uniform or Automatic binning. If you choose Uniform, the algorithm separates the interval into the specified number of bins; the range of the variable is separated into uniformly sized ranges. The upper and lower bounds for the extreme ranges include any values outside the ranges that were seen in the data.
For example, if the values for an attribute are all in the range 5-14, and you choose four ranges, the thresholds are 8 and 11, corresponding to these ranges:
≤ 8
> 8 to 11
> 11 to 14
> 14
Uniform lets you decide whether you want to specify the number of intervals or let the algorithm select a number automatically. The automatic selection of the number of bins for the uniform thresholding is based on a number of bins related to the number of distinct values: the more distinct values, the more ranges are chosen (the relationship is logarithmic).
If you choose Automatic, you also must select a discrete label. The thresholds are chosen so that the distributions of labels at different ranges are as different as possible. This approach continues to split ranges and create thresholds until no additional interval is considered significant. No interval is split if the two subintervals do not contain the minimum number of instances you can specify (this defaults to 5).
The Minimum # instances in any bin text field lets you specify the minimum number of records in any bin; this prevents the creation of bins with fewer records than the number specified.
If you click Apply, the Tool Manager picks bin thresholds and displays them in the Thresholds for selected column are text field. Output from the process of automatically computing bins appears in a popup window, showing progress of the algorithm and any errors that occur.
If you specify your own thresholds (as shown in Figure 3-9), you can choose between Use custom thresholds or Use evenly spaced thresholds by clicking either button. When you type in the thresholds, you must click Apply to make those thresholds effective for the selected columns.
The Use custom thresholds text box lets you enter the range criteria. For example, you could enter the numbers 18, 30, 50, 60. This results in the following ranges: 0-18, 19-30, 31-50, 51-60, 61+. Note that you enter only the digits and commas, not the ranges.
To specify equally spaced bins over a range of values, click the Equally Spaced Bins button. This activates the three text fields below it. You can type the start of the binning range, the end of the range, and the spacing of the bins, respectively, into these fields. If you are binning a column that is a date, you can specify units of time for the bin spacing (using the Date units popup menu under the text fields). This would permit you, for example, to bin a time period into bins of three weeks. Dates entered into these fields must be typed in the form “MM/DD/YY”. Possible time units are as follows:
years
quarters
months
days
hours
minutes
seconds
The Use custom thresholds text box accepts dates either in double quotes (as shown below), or without. If you enter dates without quotes, the quotes are added automatically.
"1/1/96", "2/1/96", "3/1/96", "4/1/96", "5/1/96", "6/1/96"
However, do not put quotes around dates used with Use evenly spaced thresholds.
Before describing the features and effects of the Aggregate button, this section provides an introduction to the concept of arrays and distribution as used in the aggregation feature.
The Aggregate button lets you perform simple aggregations (for example, sum, min, max, and so on), make arrays and distribute columns.
Table 3-1 illustrates some sample aggregations/calculations.
Table 3-1. Aggregate Example 1
State | Age_bin | Total $ Spent |
|---|---|---|
CA | 0-20 | $50 |
CA | 21-40 | $454 |
CA | 41-60 | $693 |
NY | 0-20 | $35 |
NY | 21-40 | $541 |
NY | 41-60 | $628 |
If you make Total $ Spent into an array indexed by the binned column Age_bin, the resulting table, now with only two columns, appear as shown in Table 3-2
Table 3-2. Aggregate Example 2
State | Total $ Spent [Age_bin] |
|---|---|
CA | [$50, $454, $693] |
NY | [$35, $541, $628] |
In this case, making an array reduces the number of columns by one, and also reduces the number of rows by four. Arrays are useful for the Tree Visualizer tool; they are necessary if you want to use sliders in Scatter Visualizer and Map Visualizer displays.
Distributing columns is similar, but different in several important ways. Instead of producing a single new column holding many values, distributing produces one new column for each value of the index. For example, if in the first table Total $ Spent were not made an array, but instead distributed by Age_bin, Table 3-3 would be the result.
Table 3-3. Aggregate Example 3
State | Total $_0-20 | Total $_21-40 | Total $_41-60 |
|---|---|---|---|
CA | $50 | $454 | $693 |
NY | $35 | $541 | $628 |
Thus, distributing increases the number of columns but decreases the number of rows.
If you have more than one binned column (for example, Age_bins and Sex_bin), you can make a two-dimensional array (indexed by combinations of Age_bin and Sex_bin). You also can distribute and make an array at the same time.
Table 3-4 has two binned columns: one for age, one for sex.
State | Age_bin | Sex_bin | Total $ Spent |
|---|---|---|---|
CA | 0-20 | 1 | $20 |
CA | 0-20 | 2 | $30 |
CA | 21-40 | 1 | $220 |
CA | 21-40 | 2 | $234 |
CA | 41-60 | 1 | $401 |
CA | 41-60 | 2 | $292 |
If you make Total $ Spent an array indexed by age, and remove Sex_bin, the results are shown in Table 3-5.
Table 3-5. Results When Making Total $ Spent an Array
State | Total $ Spent [Age_bin] |
|---|---|
CA | [$50, $454, $693] |
If you do not remove Sex_bin, the results are shown in Table 3-6.
Table 3-6. Results When Specifying Sex_bin
State | Sex_bin | Total $ Spent [Age_bin] |
|---|---|---|
CA | 1 | [$20, $220, $401] |
CA | 2 | [$30, $234, $292] |
If you make an array by both Age_bin and Sex_bin, the results are shown in Table 3-7.
Table 3-7. Results of Making an Array by Age_bin and Sex_bin
State | Total $ Spent [Age_bin] [Sex_bin] |
|---|---|
CA | [$20, $220, $401, $30, $234, $292] |
Finally, if you distribute by Sex_bin and index by Age_bin, the results are shown in Table 3-8.
Table 3-8. Results of Distributing Sex_bin and Indexing by Age_bin
State | Total $ Spent [Age_bin], Sex = 1 | Total $ Spent [Age_bin], Sex = 2 |
|---|---|---|
CA | [$20, $220, $401] | [$30, $234, $292] |
The examples above (with the exception of Table 3-5) had exactly one relevant value for each array element, and the distribution merely rearranged existing data values. In example of Table 3-5, there were two data values for each array element, and these were added. MineSet provides several aggregation options for datasets containing more than one value to be distributed into a given output array element. The most common option is to add the values (as done in Table 3-5). This is useful when accumulating expenditures into budgets, for example. You also can take the minimum, maximum, and average of total number of values, as well as count them.
When distributing values for a given dataset, it is possible that there are no values appropriate for a particular bin. In this case, for MIN, MAX, AVG, and SUM aggregations, the DataMover fills in a value of Null. For COUNT aggregations, the DataMover fills in a value of 0.
You can use the Aggregate button to create simple aggregations, make arrays, or distribute columns. Clicking this button causes the Aggregate dialog box to appear (Figure 3-11). It shows three lists, with the columns in the current table appearing in the middle list. If you want to aggregate, distribute, or turn a column into an array, select the name of the column, and click the left arrow button between the left and center lists. Below are popup menus that let you specify indexes (if the result is to be an array) and a distribution column (if the result is to be distributed). In addition, at the bottom of the dialog box are five toggles that let you specify how different values are to be combined when aggregated: either summed, averaged, the min or max value, or the count. When you are aggregating number-valued columns, you can choose any combination of these options. For other types, only count is permitted. If you choose more than one option, you get more than one result. For example, selecting average and max gives you one result with average values, and another one holding the max values.
The three lists of column names are given below:
Group-By columns (the default); this keeps the columns unchanged throughout the operation. For each set of records with the same combination of values in the Group By columns, only one record is output in the resulting table, with values in the aggregated columns summed, averaged, minned, maxed, or counted (depending on the checkboxes at the bottom of the panel).
Columns to remove, as can be seen with the Sex_bin column in Table 3-5
After you have finished with the additional aggregate criteria dialog box, the Current Columns text box in the Table Processing window shows the new column names that result from applying these criteria.
Some databases store numerical values as strings. Oracle stores all numbers (both integers and real numbers) in a single format, which defaults to the data type double in the Tool Manager. You can use the Change Types button to ensure that these values are processed correctly. To change the types of one or more columns, click the Change Types button. A new dialog box appears (see Figure 3-12). This dialog box contains a window with a list of column headings and their respective types.
First select a column heading in the window. Then click the Column type button. This produces a popup list of the possible types (invalid types are grayed out), as shown in Figure 3-13.
int—represents a 32-bit signed integer.
float—represents a single-precision floating-point number. The decimal point is optional when representing a floating-point number.
double—represents a double-precision floating-point number. The decimal point is optional when representing a floating-point number.
dataString—represents a string that is unlikely to appear multiple times. If it appears multiple times, several copies are made. A dataString can be used to store an address. Addresses are unlikely to be compared, and each record can have a different address.
string—represents a string of characters that can appear multiple times in the data file. Unlike a dataString, only a single copy of a given string is stored in memory, no matter how many times it appears in the data. This saves memory for strings appearing many times.
Comparing strings is also much quicker than comparing dataStrings. However, reading in strings can be slower than reading in dataStrings because it is necessary to look for duplications. An example of string use would be for a division name that appears once for each department in the division. If you are unsure whether to use a string or a dataString, use a string.
fixedString—represents a string that is stored internally as a fixed-length array of characters. These are useful when all the elements in a data column are known to be approximately the same length. They also are the most efficient way of encoding very short strings, such as State abbreviations.
unsupported—represents a database type not supported by MineSet (for example., images).
After selecting a new type, click the OK button to have the change take effect.
Note that if you try to convert an inappropriate field (such as a name) to a number, the resulting values are all zeroes.
![]() | Note: When the data source is an existing file, there are fewer possibilities available for changing any given column. |
You can use the Add Column button to create a new column whose values are computed based on a mathematical expression. For example, you could add a new column whose values are the ratio of values from two existing columns. Click Add Column to get a dialog box that lets you specify the new column name and expression (Figure 3-14).
In the upper left of this dialog box is a field for entering the new column's name. Below this is a popup menu that lets you specify the column type (integer, string, floating point, and so on).
The right-hand side of the dialog contains a large text entry area where you can type in a definition of the expression (for a complete description of the expression definition language, see “The Configuration File” in Appendix A). As a shortcut to typing column names and operators, scrolled lists in the lower left of the dialog display all columns in the current table and all possible operators. To insert a column name or operator into the expression, either double-click it in its scrolled list, or select it and click the arrow button to the right of the scrolled list.
To check the syntax of the expression you have created, click the Check Expression button. If there is an error, a dialog box appears, indicating what the error is and where it occurred. When you click OK, the expression is automatically checked, and the dialog box is not removed unless the expression is correct.
The Apply Classifier button lets you use a previously created classifier (see the “Using Mining Tools” section) to label records in the current table. For example, if you created a classifier to determine the edibility of mushrooms based on their shape, size, smell, and so on, you could use Apply Classifier to determine edibility of mushrooms in a new table. Click Apply Classifier to access a dialog box that lets you apply previously created classifiers to the current table (Figure 3-15).
On the left of this dialog box is the list of all classifiers currently available on the server. If you select a classifier, the right-hand list is filled in with the column names and types required by that classifier. If these requirements match the current table, a message at the bottom states this, and the OK button is activated. If the current table does not have all the columns required for the selected classifier, the message at the bottom states this, the columns that are missing are selected in the list on the right, and the OK button is deactivated. Finally, a text field at the bottom lets you name the new column to be created by the classifier.
Table processing is a series of operations performed by using the buttons described above. To allow you to see this series of steps, and go back if you made a mistake, there are two Table History buttons at the bottom of the Table Processing panel (Figure 3-16). When you click the left arrow button, the columns window shows the table as it appeared at an earlier step. Clicking the right arrow button brings the table forward to its current state.
To the right of the history buttons is the information field Current view is, which counts the changes you've made and indicates which step you are viewing. The two integers in this field indicate which table view you're looking at, out of the total number of table views that exist. For example, if you've made two changes, you can view the original table (1 of 3), the table after the first change (2 of 3), or the table after the second change (3 of 3).
As you go back and forth using the Table History buttons to view earlier versions of the table, the Prev and Next fields (under the arrow buttons) help you keep track of where you are in the history of the table. For any table you view, the Prev: field tells you what the previous change was, and the Next: field tells you the next change.
The Edit Prev. Op. button allows you to edit the operation shown in the Prev. field. (This button does not work when Current view is: 1 of n, because that is the original table, with no previous changes.) When you click the Edit Prev. Op. button, the dialog box for the previous operation comes up, and you can make changes to that operation. For example, if the previous operation was binning columns, when you click Edit Prev. Op., the Bin Columns dialog box appears.
Note that by changing a previous operation, you could affect operations you set up subsequent to the current one. For example, if you delete a column that you used in a subsequent binning operation, that binning operation becomes invalid. The Edit History button can help you avoid problems.
When you click the Edit History button, the Table History dialog box appears and shows you the complete history of the Data Transformation table (Figure 3-17). Each version of the table appears as a box containing a list of the columns, linked by a smaller box (indicating the operation performed on the table) to the next version of it.
As with Edit Prev. Op, changing one operation usually affects (sometimes invalidates) subsequent operations in the history.
Under the window displaying this flow chart are the zoom buttons that allow you to view the flow chart closer up or farther away. You can choose the zoom by using the button indicating the percentage, or by clicking the arrow buttons to increase or decrease the size. The increments of change are the same whether you use the percentage button or the arrow buttons.
Next to the zoom buttons is a toggle button that allows you to view the flow chart vertically or horizontally. Clicking the button switches you back and forth between the two points of view.
Under the indicator For Selected Operation is a row of buttons that becomes active if you click one of the operations in the flow chart. Once you select an operation, you can alter it. The Edit Op button brings up the dialog box for the selected operation, so you can make changes to it. The Delete Op button will remove the operation from the table history, and the elements that follow in the flow chart move over when it disappears. The Add New Op. Before and Add New Op. After buttons let you insert a new operation into the table history.
If you decide not to carry out these changes, click Discard Changes; the changes you made are ignored, and you return to the Data Transformation panel. You might choose Discard Changes if, for example, you delete a column that was used in a subsequent binning operation, and the binning operation and linked table also disappear. If that consequence was not what you wanted, Discard Changes allows you to undo your choice.
If the changes you've made in Table History are what you want, click Apply History Changes to implement the changes and return to the Data Transformation panel.
The Data Destination panel (Figure 3-18) lets you direct your processed data to one of the SGI_MineSet visualization or mining tools or to a data file.
There are three tabs at the top of this panel:
Viz Tools
Mining Tools
Data Files
These are the three possible destinations for your data. They are discussed in greater detail in later chapters dealing with the Data Destination tools.
If you choose the Viz Tool tab, the visualization tool panel appears under Data Destination.
Viz Tool is a popup menu that lets you choose among Tree Visualizer, Map Visualizer, Scatter Visualizer, and Text Editor to determine the type of visual representation you want for your data. The first three tools are described in their respective chapters. The Text Editor tool launches a text editor in a separate window for viewing the raw data file. The Text Editor has no requirements and no options. The specific editor that is launched defaults to jot. If the user has the WINEDITOR environment variable, that editor is used. If WINEDITOR has not been set, but EDITOR has, that editor is used.
Tool Options—lets you further specify options you want to set in the specified tool's configuration file.
Clear Selected—lets you undo the mapping to a selected Visual Element.
Clear All—clears all mappings.
Invoke Tool—lets you start the tool you specified (via the top button) using the configuration file named in the Saved as text field.
Each tool's requirements are listed individually in the Visual Elements pane. This pane lets you map a table column to a requirement. To do this,
Select a column by clicking its name in the Current Columns pane.
Select the requirement which you want to map the column by clicking on that requirement in the Visual Elements pane.
The Viz Tool panel now shows the Visual Element and the column to which it has been mapped (see Figure 3-19).
You can clear the mapping at any time by selecting the requirement that has the mapping you want to change, then clicking the Clear Selected button. You can clear all mappings using the Clear All button.
If you want to specify other details to fine-tune your mappings or to change the settings so that the data representations more clearly reflect your intentions, click the Tool Options button. A dialog box specific to each MineSet tool appears, where you can manually specify the options to use.
![]() | Note: For details on a specific tool's options, see that tool's chapter. |
The MineSet Classifiers are described in Chapter 8, “MineSet Inducers and Classifiers,” Chapter 9, “Inducing and Visualizing the Decision Tree Classifier,” and Chapter 10, “Inducing and Visualizing the Evidence Classifier.” Column importance is described in Chapter 11.
If you click the Associations tab, the panel lets you take the data file you created in Data Transformations and proceed to the Rule Visualizer. Each step of the process is shown in the boxes in the panel:
If you don't want to go through this process manually, click the Run Rule Viz button, and the computer will perform the process using defaults.
Column Importance (Figure 3-21) determines how important various columns are in discriminating the different values of the label column you choose. You might, for example, want to find out the best three columns for discriminating the label good credit risk so you can choose them for the Scatter Visualizer. When you select the label and click Go!, a popup window appears with the three columns that are the best three discriminators. A measure called “purity” (a number from 0 to 100) informs you how well the columns discriminate the different labels. Adding more columns can only increase the purity.
There are two modes of column importance:
Simple Mode
To invoke the simple mode, choose a discrete label from the popup menu, and specify the number of columns you want to see.
Advanced Mode
Advanced mode lets you control the choice of columns. To enter advanced mode, click Advanced in the Column Importance panel. A dialog box appears, as shown in Figure 3-22. The dialog box contains two lists of column names: the left list contains available attributes, and the right list contains attributes chosen as important (by either the user or the column importance algorithm).
Advanced mode can work two different ways: finding several new important attributes, or ranking available attributes.
Finding Several Important Attributes
To enter this sub-mode, click the first of the two radio buttons at the bottom of the dialog (...find [number] additional important attributes). If you click Go! with no further changes, the effect is the same as if you were in Simple Mode, finding the specified number of important columns and automatically moving them to the right column. Near each column, the cumulative purity is given (that is, the purity of all the columns up to and including the one on the line). More attributes can only increase the purity.
Alternatively, by moving columns names from the left list to the right list, you can pre-specify columns that you want included and let the system add more. For example, to select the age column and let the system find three more columns, click the age column name, then click the right arrow.
Clicking Go! lets you see the cumulative purity of each column, together with the previous ones in the list. A purity of 100 means that using the given columns, you can perfectly discriminate the different label values.
Ranking Available Attributes
Advanced Mode also lets you compute the change in purity that each column would add to all those that were already selected. For example, you might choose age, and then ask the system to compute the incremental improvement in purity that each column would yield.
To enter this sub-mode, click the second of the two radio buttons at the bottom of the dialog (...compute improved purity for attributes on the left.). This sub-mode permits fine control over the process. If two columns are ranked very closely, you might prefer one over the other (for example, cheaper to gather, more reliable, easier to understand).
Column Importance Notes
Note that with other columns, the importance of features varies from their ranking alone. For example, while net-income might be a good column individually, it might not be as important together with salary because they are likely to be highly correlated. The best set of three columns is not necessarily composed of the columns that rank highest individually. If two columns give the income in dollars and in another currency, they are ranked equally alone; however, once one of them is chosen, the other adds no discriminatory power to the set of best features.
Column selection is useful for finding the best three axes for the Scatter Visualizer, as well as for finding a good discriminatory hierarchy for the Tree Visualizer.
All floating point values (double or float) are pre-discretized using the automatic discretization. If a column has no value given to it in the left list, the algorithm did not consider it, because it either had a single value (for example, when it is discretized into one interval), or the number of records that it would separate are not statistically significant.
The Tool Manager lets you save the manipulated table for future use in a data file on the client or server. If you click the Data Files tab, the panel shown in Figure 3-23 appears.
The two toggle buttons in this panel let you specify whether the file is to be saved on the server or your client machine. The selected name for the client file appears next to the Client checkbox. If you select Client, the Choose new client file button brings up a dialog for you to choose the name for the client file. If you select Server, you can type the server filename directly into the adjacent text field.
![]() | Note: Pathnames are not permitted for server files; all server files are stored in the Datamove cache directory. |
The Tool Manager can save a description of your work to a “session file” for future use. A session file contains a description of the data source you selected, all the transformations on the data, and the mining or visualization of the data. Each session file can hold descriptions of only one data source and one data destination; thus, if you change the destination visual tool or source data table, the session file loses its links to any previous data source or destination.
Session files can be saved at any time through the entries in the File menu, described below. The name of the current session appears in the window's title bar. The Tool Manager also keeps a parallel session file, called .latest.mineset, in your home directory. It always has a record of your most recent actions in the Tool Manager. Whenever you start the Tool Manager without a session file, it reads the contents of the .latest.mineset file to return you to the state when you last ran MineSet.
Session files also can be used for running the Tool Manager in batch mode, by issuing this command at the UNIX shell prompt:
mineset_batch [-s serverPassword -d databasePassword] configFile |
The -s and -d options allow you to specify the password for logging into the server and database respectively. If you do not specify these options, mineset_batch will ask you to type in the passwords, thus these options are useful when running mineset_batch from a shell script. To specify that there is no password for either the server or database, use -s or -d followed by two double quotes, that is,
mineset_batch -s "" -d "" foo.mineset |
If you specify one of the two passwords, you must specify both.
In batch mode, the Tool Manager does not bring up tools or windows; however, it creates files for tools. For example, if the session file includes the Tree Visualizer as the data destination, running the Tool Manager in batch mode produces files for running the Tree Visualizer, but the Tool Manager does not invoke it.
At the top of the screen are four pulldown menus:
File
Options
Visual Tools
Help
The following sections, describe each of these menus.
The File menu lets you choose what to do with your current session, which is one complete session with a tool. This includes choosing the server, data source and table, all the table manipulations, and the mapping or classifying of the data.
The File menu provides five functions:
New—Lets you start a new session, by setting your session name to “untitled.”
Open—Lets you restore a session that has been saved previously.
Preferences—Lets you set two global Tool Manager options. When you choose this menu item, a dialog box appears with two toggles. The first, Include NULLs in aggregation arrays, specifies whether arrays and distributions created using the Aggregation dialog (see “Aggregation”) have a slot for null values. The second, Automatically restore session on startup, determines whether the Tool Manager tries to return you to the same place the next time you start up.
The Options menu has only one item: Database Explorer. Choosing this item brings up the Database Explorer (Figure 3-25), a tool to help you find out about the information in database tables. For example, it can be used to find maximums, minimums, averages, sums, and distinct values.
The Database Explorer has three panels: the top lets you select the table and columns, the middle provides summary information, and the bottom provides summary results. The upper left shows a list of all the tables in the current database. Selecting any of these tables causes the columns of that table to appear in the list to the right. To get summary information, select one or more column names, and click the desired operations in the center panel. When you click the Submit Query button in the middle panel, the summary information is computed for the specified columns (this can take a long time for large tables). Results are output at the bottom. Note that min, max, average, and sum can be applied to number-valued columns only.
![]() | Note: The count option in the Database Explorer produces a count of all rows in the table, not just those with non-null values. |
The Visual Tool menu allows you to invoke any of the visual tools directly:
Evidence Visualizer
Map Visualizer
Rule Visualizer
Scatter Visualizer
Tree Visualizer
If you've created a file that runs within one of these tools and you want to go back to it, click the tool. From within the tool, use File Open to open the data file.
The Help menu provides information about the elements of the Tool Manager and how they work:
Click for Help—Gives help information about a particular item if you press Shift-F1, then click the item for which you want help.
Overview—Gives an overview of the online help and how to use it.
Index—Provides an index of the complete help system. This option is currently disabled.
Keys & Shortcuts—Provides the keyboard shortcuts for all of the Tree Visualizer's functions that have accelerator keys.
Product Information—Indicates what version of the Tool Manager you are using.
MineSet User's Guide—Invokes the IRIS Insight viewer with the online version of this manual.
Many of the tool option dialogs have options for choosing colors. MineSet 1.1 has a color list chooser that uses color swatches. This section describes how to choose, apply, and change color options for the MineSet Visualizers.
If only one color is to be chosen (for example a grid color), a single color swatch appears (Figure 3-26).
Clicking the swatch brings up a Color Browser that lets you change the color of that swatch (Figure 3-27). The Color Browser is described in more detail in the “Using the Color Browser” section, shown in Figure 3-27.
If a list of color swatches is to be chosen, the list of swatches appears (these can be empty initially), as shown in Figure 3-28.
To edit the color, click a swatch with the left mouse button. This also selects the swatch for making changes to the colors with the buttons. If you click with the swatch with the middle mouse button, the swatch is selected, but the color chooser does not appear.
Next to the list of swatches are four buttons. First is the odd button, labeled with a plus sign (+), adds a new color at the end of the list. A swatch is added, and the color chooser appears, where you can select the color of that swatch. The add button is disabled if the maximum number of colors is already in the list.
Next to the add button is a delete button, labeled with a minus sign (-). This button deletes the selected color. It is disabled if no swatch is selected, or if the list already has the minimum number of colors.
Next to the delete button are two buttons to shift the selected color right and left. These buttons are disabled if no swatch is selected, or if the swatch is already at the end of the list.
If there are more colors in the list than room to display them, scroll arrows are added at each end of the list (Figure 3-29).
If the hardware runs out of colors, the color swatches are replaced with text labels showing the color in X notation (Figure 3-30).
The Color Browser (Figure 3-27) appears when you click a color swatch or the add button in the Colors panel of the visualizer's Configuration Options panel.
To select a color using the Color Browser:
Move your mouse cursor on top of the small circle in the colored hexagon.
Press the left mouse button, and move your mouse around the hexagon. The color beneath the small circle appears in the rectangle next to the Current Color label. This rectangle acts as your color palette while you choose a color.
Release the mouse button when the small circle is on top of a color you want. The selected swatch immediately takes on the chosen color.
You can edit several colors without dismissing the Color Browser; clicking any color in the options panel lets you edit that color in the already posted Color Browser.
Click the OK button when you decide on a color. The Color Browser window closes.