Data Mining Architecture
Data mining is a process used to discover useful and previously unknown
information from large amounts of data. It helps organizations analyze data and find patterns
that support better decision-making.
A data mining system consists of several components that work together
to collect, process, analyze, and present data. These components form the data mining system
architecture.
Data Sources
Data sources are the places where the data is originally collected.
Common data sources include databases, data warehouses, the World Wide Web (WWW), text
files, spreadsheets, and other documents.
For data mining to be effective, a large amount of historical data is
required. Organizations usually store this data in databases or data warehouses.
A data warehouse may contain data from multiple databases,
spreadsheets, or text files. Sometimes even simple files like Excel sheets can contain useful
information. The internet and the World Wide Web are also important sources of data.
Data Processing (Cleaning, Integration, and Selection)
Before data is used for mining, it must go through several preprocessing
steps.
Since data comes from different sources and formats, it may contain
errors, missing values, or unnecessary information. Therefore, the data
must first be:
- Cleaned – removing errors, missing values, and incorrect data.
- Integrated – combining data from different sources into a single dataset.
- Selected – choosing only the relevant data needed for analysis.
These steps ensure that the data used for mining is accurate and
meaningful. Data preprocessing can be complex because different methods are
used to prepare the data properly.
Database or Data Warehouse Server
The database or data warehouse server stores the processed data that is
ready for analysis.
This server manages the data and retrieves the required information when a
user requests a data mining task. It acts as the main storage system for the
mining process.
Data Mining Engine
The data mining engine is the core component of the data mining system.
It performs the actual analysis of the data.
It includes several modules that carry out different types of data mining
tasks such as:
- Association – finding relationships between data items
- Characterization – summarizing general features of data
- Classification – grouping data into predefined categories
- Clustering – grouping similar data together
- Prediction – forecasting future values
- Time-series analysis – analyzing data collected over time
The data mining engine uses tools and software to extract meaningful
insights from the stored data.
Pattern Evaluation Module
The pattern evaluation module checks the patterns discovered during the
data mining process.
It determines which patterns are interesting or useful by using certain
evaluation measures or thresholds. Patterns that do not meet the required criteria are
removed.
This module works closely with the data mining engine to focus only on
valuable patterns and improve the efficiency of the mining process.
Graphical User Interface (GUI)
The Graphical User Interface (GUI) allows users to interact with the data
mining system easily.
Through the GUI, users can:
- Give queries or tasks to the system
- Control the mining process
- View the results and visualizations
The GUI hides the technical complexity of the system and makes it
easier for users to operate.
Knowledge Base
The knowledge base stores background information that helps improve
the data mining process.
It may include:
- Domain knowledge
- User preferences
- Previous mining results
- Rules or patterns discovered earlier
The knowledge base helps guide the mining process and improves the
accuracy of the results. It also interacts with the pattern evaluation module to update and
refine knowledge over time.
