Architecture of Data Mining-The major component of data mining

Architecture of Data Mining

Architecture of Data mining is very important process for creating useful patterns for user from big data. there are number of components used in data mining for processing data.

The major component of data mining is-

1.Data Source

2.Data Warehouse

3.Data Mining Engine

4.Pattern Evaluation Model

5.GUI

6.Knowledge Base

1.Data Source-

In Data source data coming from largest database system like different data warehouses, www, text files or other any types of document like video, audio, 2D, 3D file or any type of file which are stored in data source. that data is large volume of data and also include a historical data about that organization, that all data stored in in data sources or operational databases.

In that data sources include lots of Mini databases but that data sources can not give a proper knowledge for user to giving a decision, so that big data source is unstructured, Before that data passes to data mining need cleaning of that data is important. That data was cleaned and integrated and then data passes to in next stage.

2.Data Warehouse-

In data warehouses stores the actual data sources that data is passes to the data mining but all data is clean for process. In data warehouse only save useful data and correct data.

3.Data Mining Engines-

Data mining engine include data mining algorithms like classification, clustering, regression, time series analysis, prediction, sequence series-that all algorithm methods are stored in data mining engine. These all algorithm used with requirement.

4.Pattern Evaluation Model-

Pattern evolution is attached to data mining engines. Data mining engine and pattern evaluation decide which pattern is created from large database with user requirement.

5.Graphical User Interface GUI-

GUI model communicate between user and data mining system, this model helps to user for understanding result. GUI model interact with data mining tool and create a user understandable format result with use of different GUI techniques with diagrammatic and graphical representation.

6.Knowledge Base-

In knowledge base saved previously created patterns. If data mining working start then knowledge base is help to pattern valuation and graphical user interface for selecting best algorithm and creating user friendly pattern for user. knowledge base is important part from data mining architecture because previous all patterns are saved in knowledge base, so knowledge base is select reliable and accurate algorithm and pattern for user because of that user get result with more accurate and reliable.

Explanation :

The architecture of data mining defines the structure, components, and flow of processes involved in discovering useful patterns, trends, and knowledge from large datasets. It serves as the framework that supports the extraction of valuable information through analytical techniques and algorithms. A typical data mining architecture consists of several key components, including the data sources, data warehouse, data mining engine, pattern evaluation module, and graphical user interface (GUI). Each component plays a specific role in the overall data mining process.

1. Data Sources:

Data mining begins with data collected from multiple sources such as databases, data warehouses, flat files, web data, or transactional systems. The data may be structured, semi-structured, or unstructured. Before mining, this raw data is cleaned and integrated to ensure accuracy and consistency.

2. Data Warehouse:

The data warehouse acts as a central repository where cleaned and integrated data is stored. It organizes data in a structured format suitable for analytical operations. Data from different sources is extracted, transformed, and loaded (ETL) into the warehouse to support efficient mining and querying.

3. Data Mining Engine:

This is the core component of the architecture. The data mining engine uses algorithms and techniques such as classification, clustering, association, regression, and anomaly detection to uncover hidden patterns and relationships within the data. It performs complex computations and model-building processes essential for knowledge discovery.

4. Pattern Evaluation Module:

Once patterns are identified, the pattern evaluation module assesses their validity, relevance, and usefulness. It removes redundant or insignificant patterns and retains only those that provide meaningful insights for decision-making.

5. Graphical User Interface (GUI):

The GUI provides a user-friendly platform for interaction between the user and the data mining system. It allows users to select data, apply mining algorithms, visualize results, and generate reports easily.

Read More-

1. What Is Data Warehouse
2. Applications of Data Warehouse, Types Of Data Warehouse
3. Architecture of Data Warehousing
4. Difference Between OLTP And OLAP
5. Python Notes