The architecture of a typical data mining system may have the following major components Database, data warehouse, World Wide Web, or other information repository:
This is one or a set of databases, data warehouses, spreadsheets, or other kinds of information repositories.
Data cleaning and data integration techniques may be performed on the data.
Database or data warehouse server:
The database or data warehouse server is responsible for fetching the relevant data, based on the user’s data mining request.
This is the domain knowledge that is used to guide the search or evaluate the interestingness of resulting patterns.
Such knowledge can include concept hierarchies, used to organize attributes or attribute values into different levels of abstraction.
Knowledge such as user beliefs, which can be used to assess a pattern’s interestingness based on its unexpectedness, may also be included.
Data mining engine:
This is essential to the data mining system and ideally consists of a set of functional modules for tasks such as characterization, association and correlation analysis, classification, prediction, cluster analysis, outlier analysis, and evolution analysis.
Pattern evaluation module:
This component typically employs interestingness measures and interacts with the data mining modules so as to focus the search toward interesting patterns.
It may use interestingness thresholds to filter out discovered patterns.
Alternatively, the pattern evaluation module may be integrated with the mining module, depending on the implementation of the data mining method used.
For efficient data mining, it is highly recommended to push the evaluation of pattern interestingness as deep as possible into the mining process so as to confine the search to only the interesting patterns.
This module communicates between users and the data mining system, allowing the user to interact with the system by specifying a data mining query or task, providing information to help focus the search, and performing exploratory data mining based on the intermediate data mining results.
In addition, this component allows the user to browse database and data warehouse schemas or data structures, evaluate mined patterns, and visualize the patterns in different forms.