"General Query Settings"

• "Class/Examined Field Selection" list - a list of fields from the table, currently selected on the Data Set Input page. Determine the Class field by selecting a field from the list. It is recommended to select fields, containing the most important data. For example, it could be a field, that contains information that marks loyal or fraudulent customers, important financial data.
• "Selection of Fields" control - contains a list of fields from the table, currently selected on the Data Set Input page. The list doesn't include fields of unsupported types and the field, selected as the "Class/Examined Field". Fields, marked in the list, can be used for creating Decision Rules and Decision Trees.  Note: Marking the fields is necessary for data mining (Rules or Decision Trees creation). The Statistics can be obtained without marking the fields.

After selecting the Class/Examined Field the program specifies the selected field's type. In case if the field is of text or integer type, the following controls will be displayed:

• "Classes List" list - depending on the Class Field type it will contain a list of determined classes. Select Classes to be analysed.
• "Total Number of Classes:" - displays the overall number of Classes.

If the examined filed is of float type, the Classes list control is replaced with controls for float Classes creation.

With the help of dispayed controls you can create a new class manually, by puting it's minimum and maximum values into corresponding fields and pressing the "Add Class to the List" button.

You can also remove the selected class from a list, by pressing the "Delete from the List" button or clear all classes with the "Clear List" button.

Press "Create Classes" button after forming the list of classes. This will save the list for further analysis.

Rules Query Settings

The "Rules Query Settings" group box contains the following controls:

• "Element belongs to class(%)" - input the probability for Decision Rules.
• "Minimum number of cases for a Rule" - the rules, describing less than minimum  number of cases, will be ignored by the program.
• "Float fields minimum value" - determines minimum float values that Estard Data Miner will use for the analysis.
• "Create full Rules" control - marking this control will make Estard Data Miner to find all Decision Rules, without cutting off unnecessary elements.
• "Create minimized Rules" control - marking the control will allow to create minimized Rules, enougth for Classes analysis. The overall number of Rules might be less than with "Create full Rules" option marked.
• "Maximum Rules number for a Class" - this control allows to limit the number of created Rules. For example, if setting controls value to 10, the program will create no more than 10 Rules for each Class.

Decision Tree Query Settings

The Decision Tree can be created in various ways.

• "Build Automatically" control- mark it if you want the program to decide how to create the Decision Tree.
• "Build Tree for Each Field" control - mark it if you want to recieve a Decision Tree for each Field, marked in the "Selection of Fields" control. Time, necessary to obtain Decision Trees is proportional to number of Fields used in analysis processing.
• "Build starting with Field" control - mark it to create a concrete Decision Tree, strarting with the Field selected in the list below the control.
• "Build a Single Tree"  - in case if some Classes were not defined after building the first Tree, Estard Data Miner will try to build a new Tree. Marking this control will make Estard Data Miner to create a single Decision Tree.
• "Stop After defining All Classes" control - will stop Decision Trees creation in case if all Classes were defined.

Decision Tree Output Settings:

• "Clear previous Trees before adding new ones" option - clears up the displayed Decision Trees before adding new Decision Trees.
• "Add new Decision Trees to previously created"  - if some Decision Trees are displayed in the control on the "Decision Tree Output" page, they won't be removed when creating a new Tree.