Data Mining Architecture
Data mining is a crucial technique for uncovering previously hidden and potentially valuable information from large datasets. The process of data mining encompasses several key components, which together form the architecture of a data mining system.
The key components of data mining systems include the data source, the data mining engine, the data warehouse server, the pattern evaluation module, the graphical user interface, and the knowledge base.
Data Source:
The primary sources of data for mining include databases, data warehouses, the World Wide Web (WWW), text files, and other documents. To achieve effective data mining, a substantial amount of historical data is essential. Organizations often store data in databases or data warehouses, which can include multiple databases, text files, spreadsheets, or other data repositories. Additionally, plain text files or spreadsheets may also hold valuable information. The World Wide Web and the internet serve as another significant source of data.
Different processes:
Before data can be passed to the database or data warehouse server, it must undergo cleaning, integration, and selection. Since data often comes from diverse sources and in various formats, it is typically incomplete and may contain inaccuracies, making it unsuitable for direct use in data mining. Therefore, the initial steps involve cleaning and unifying the data. This process includes gathering more information than necessary from multiple sources and then selecting only the relevant data for the server. These procedures are complex and involve various methods for effective data selection, integration, and cleaning.
Database or Data Warehouse Server:
The database or data warehouse server houses the original data, which is prepared for processing. Consequently, the server is responsible for retrieving relevant data based on user requests related to data mining.
Data Mining Engine:
The data mining engine is a central component of any data mining system. It includes multiple modules designed to perform various data mining tasks, such as association, characterization, classification, clustering, prediction, and time-series analysis.
In essence, data mining is the core of the data mining architecture. It encompasses the tools and software necessary to extract insights and knowledge from data collected from diverse sources and stored in the data warehouse.
Pattern Evaluation Module:
The Pattern Evaluation module is chiefly responsible for assessing patterns by applying a threshold value. It works in conjunction with the data mining engine to refine the search towards interesting patterns.
This module typically uses significance measures that interact with the data mining modules to direct the focus toward noteworthy patterns. It may employ a threshold to filter out less relevant patterns. Additionally, the pattern evaluation module might be integrated with the mining module, depending on the specific data mining techniques employed. For effective data mining, it is generally recommended to integrate pattern evaluation as deeply as possible into the mining process, thereby narrowing the search to only the most compelling patterns.
Graphical User Interface:
The Graphical User Interface (GUI) module serves as the bridge between the data mining system and the user. It enables users to interact with the system easily and efficiently, without needing to understand the underlying complexity. This module works with the data mining system to process user queries or tasks and presents the results in a user-friendly manner.
Knowledge Base:
It sounds like you’re describing a system where a knowledge base supports the data mining process by providing context, user feedback, and insights to enhance pattern discovery and result accuracy. Here’s a breakdown of how this might work:
Knowledge Base Role:
- Guiding Search: The knowledge base can offer predefined queries or patterns that help narrow down the search space, making data mining more efficient.
- Evaluating Results: By incorporating historical data and user experiences, the knowledge base can help assess the relevance and validity of discovered patterns.
- Updating and Refining: The knowledge base itself evolves based on new data and user feedback, improving the system’s accuracy and reliability over time.
