KDD Process (Knowledge Discovery in Database):

• The term KDD refers to the broad process of finding knowledge in data, and emphasizes the high level application of particular data mining methods.
• The goal of the KDD process is to extract knowledge from data in the context of large databases.

• The overall process of finding and interpreting patterns from data involves the repeated application of the following steps:

1. Developing an understanding of:

   • The application domain
   • The relevant prior knowledge
   • The goals of end user

2. Creating a target data set:

   • Selecting a data set or focusing on a subset of variables or data samples on which discovery is to be performed.

3. Data cleaning and preprocessing:

   • Removal of noise or outliers.
   • Strategies for handling missing data fields.

4. Data reduction and projection:

   • Finding useful features to represent the data depending on the goal of the task.

5. Choosing the data mining task:

   • Deciding whether the goal of the KDD process is classification, regression, clustering, etc.

6. Choosing the data mining algorithm:

   • Selecting methods to be used for searching the pattern in the data.
   • Deciding which models and parameters may be appropriate.
   • Matching a particular data mining method with the overall criteria of the KDD process.

7. Data mining:

   • Searching for patterns of interest in a particular representational form or a set of such representations as classification rules or tress, regression, clustering, and so forth.

8. Interpreting mined patterns

9. Consolidating discovered knowledge

Architecture of Typical Data mining system

• Architecture of a typical data mining system may have the following major components as shown in fig:

1. Database, data warehouse, or other information repository:

   • This is information repository.
   • Data cleaning and data integration techniques may be performed on the data.

2. Databases or data warehouse server:

   • It fetches the data as per the users’ requirement which one need for data mining task.

3. Knowledge base:

   • This is used to guide the search, and gives the interesting and hidden patterns from data.

4. Data mining engine:

   • It performs the data mining task such as characterization, association, classification, cluster analysis etc.

5. Pattern evaluation module:

   • It is integrated with the mining module and it give the search of only the interesting patterns.

6. Graphical user interface:

   • This module is used to communicate between user and the data mining system and allow users to browse databases or data warehouse schemas.