Introduction to Data Mining

• Getting Started With Data Mining
• Rules of Use
• FAQ

Step By Step Guide

• Step by Step Guide
• Query Types
• Working with Classes
• Using Rules and Decision Trees Settings
• Viewing Field Values

Program Interface

• The ESTARD Data Miner Menu
• Managing Panel
• Query Wizard
• ESTARD Data Miner Pages
• "Database" Page
• "Statistics" Page
• "Rules" Page
• "Decision Tree" Page
• "Initial Query" and "Query Parameters" Pages
• "What-If" Page
• "Search a Database" Page

Using Databases

• Learning Database
• Analysed Database
• Database Types
• Database Loading
• Loading a Database Using ODBC Drivers

Using Rules & Decision Trees

• Query Settings
• Using Rules
• Editing Rules
• Using Decision Trees

BI Functions

• Selecting BI Data to Use
• Fields Mapping Settings
• Patterns Searching: Simple Select Query

Reporting & Saving 

• Decision Trees and Rules Reports
• ESTARD Data Miner Files

Using Decision Trees

Decision Tree is a data mining method that allows to create easy to understand and use decision models.

The decisions are represented as the nodes of the tree model.  The results of the decisions are outputed as the final "leafs" of the tree.

Decision Tree Advantages

Amongst other data mining methods, decision tree is the method that has several advantages:

• Intuitively comprehensible classification model. People are able to understand decision tree models after a brief explanation.
• Data preparation for a decision tree is basic or unnecessary. Other techniques often require data normalisation, dummy variables need to be created and blank values to be removed.
• Rules generation in the fields where experts have difficulties with formalizing their knowledge.
• Is a white box model. If a given situation is observable in a model the explanation for the condition is easily explained by a simple boolean logic.
• It is easy to validate a model using statistical tests and other data mining methods. That makes it possible to account for the reliability of the model.
• Is robust, perform well with large data in a short time. Large amounts of data can be analysed using personal computers in a time short enough to enable stakeholders to take decisions based on its analysis.

Because of these and many other reasons, methodology of decision trees is an important tool for any scientist dealing with data analysis, no matter if he is theorist or practician.

Areas of Decision Trees Application

Decision trees are an excellent tool in decision-making and data mining systems. They can be of good service to any analyst, manager or scientist.

Decision trees are successfully used to solve real-world problems in the following fields:

• Banking. Estimation of clients creditworthiness when giving credits.
• Insurance Industry. Detecting possibly fraudulent clients in customers databases.
• Medicine. Diagnostics of various diseases.
• Molecular biology. Analysis of amino acids composition.