Data Mining Architecture | Data Mining tutorial

              Data mining Architecture

Introducation:

In this tutroial learn Data Mining Architecture. previous tutorial can what is data mining with example follow this link what is data mining with example

Data mining derives its name from the similarities between searching for valuable business information in a large database - for example, finding linked products in gigabytes of store scanner data -- and mining a mountain for a vein of valuable ore. Both processes require either sifting through an immense amount of material, or intelligently probing it to find exactly where the value resides. Given databases of sufficient size and quality, data mining technology can generate new business opportunities by providing these capabilities

What Is Data Mining?

Data mining refers to extracting or mining knowledge from large amounts of data. The term is actually a misnomer. Thus, data mining should have been more appropriately named as knowledge mining which emphasis on mining from large amounts of data.

 What is data mining architecture?

Data mining is a very important process where potentially useful and previously unknown information is extracted from large volumes of data. ... The major components of any data mining system is a data source, data warehouse server, data mining engine, pattern evaluation module, graphical user interface and knowledge base.

What is data mining techniques?

Data Mining Techniques. There are several major data mining techniques have been developing and using in data mining projects recently including association, classification, clustering, prediction, sequential patterns, and decision tree. We will briefly examine those data mining techniques in the following sections.

1. Knowledge Base:

This is the domain knowledge that is used to guide the search 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. Other examples of domain knowledge are additional interestingness constraints or thresholds, and metadata (e.g., describing data from multiple heterogeneous sources).

2. 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.

3. Pattern Evaluation Module:

This component typically employs interestingness measures 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 as to confine the search to only the interesting patterns.

4. User interface:

This module communicates between users and the data mining system allowing the user to interact with the system by specifying a data mining query a 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.