Understanding Data Mining Architecture

Written by Coursera Staff • Updated on

Explore the intricacies of data mining architecture, the types of data mining architecture, and the different tiers and learn about its various components.

[Featured Image] Two IT professionals discuss the results of data mining architecture as they complete a project at work.

Data mining is the process of extracting valuable patterns and interesting information from vast amounts of data. Such data sources could include databases, the internet, data warehouses, other information repositories, or data uploaded into the system.

Data mining is a process that can be used on almost any data as long as it is relevant to the application targeted. Database data, transactional data, and data warehouse data are mining applications' most fundamental data types.

Types of data mining architecture

Data mining architecture can be broken down into four types. Below, we define each source and how each is used:

No-coupling data mining

The data mining system in this architecture does not use any database features but retrieves data from specific data sources, such as a file system. No-coupling architecture is usually considered poor for systems that use data mining. Instead, it is only used for simple data mining procedures.

Loose coupling data mining 

This data mining system retrieves data from a database or data warehouse and records the outcome of the system from which it was taken. It is a memory-based data mining system. It doesn’t necessitate high performance or scalability.

Semi-tight coupling data mining

Its data warehouse capabilities include indexing, sorting, and aggregation. This architecture allows the database to retain an intermediate result for improved performance.

Tight coupling data mining 

A data warehouse is generally regarded as an information retrieval component in tight coupling. Data mining tasks are efficiently carried out using the entirety of a database's or data warehouse's features. This architecture offers excellent performance, integrated information, and system scalability. This approach divides the data mining architecture into the data, application, and front-end layers.

  • Data layer: The data layer can be a data warehouse system or database. All data sources interface with this layer, which stores the data mining findings. You can present this to the end user using reports or another type of visualisation.

  • Data mining application layer: This layer extracts information from a database. The data is transformed into the desired format and processed using different mining algorithms. 

  • Front-end layer: It offers a user-friendly interface that helps users interact easily with the data mining system. The user is shown data mining results in visualisation form at the front-end layer.

Components of data mining architecture

We need a powerful data mining system with a strong architecture that can interact smoothly with all elements to quickly analyse complex and large amounts of information. The main components of a data mining architecture include:

Data sources

The data sources are the internet, data warehouses, databases, text files, and other publications. For data mining to be practical, you require ample historical data. Businesses generally keep data in data warehouses or databases. 

A data warehouse includes text files, spreadsheets, database(s), or other data repositories. Even spreadsheets and plain text files can occasionally include information. The internet is also an important data source.

Data cleaning, integration, and selection

Data originating from numerous sources and in different forms may need to be revised. Therefore, before sending the data to the data warehouse or database, the data goes through a cleaning, integration, and selection process. This process helps choose the relevant data and sends it to the server. 

Database server

The database consists of data ready for processing. The server manages and retrieves that data upon the user's request. 

Data mining engine

This is the key component of every data mining architecture. It has several modules that can be easily used for data mining tasks, such as classification, association, characterisation, time-series analysis, clustering, prediction, etc. 

Pattern evaluation module

This component uses a threshold value to explore a pattern. It utilises stake measures and collaborates with the data mining engine to identify interesting and relevant trends and patterns in the data. 

This module can coordinate with the mining module based on data mining methods. To uncover the desired patterns and ensure a successful data mining process, it is helpful to integrate the evaluation of pattern stakes as feasible into the mining technique.

Graphic user interface

The graphic user interface (GUI) module simplifies the user's interaction with data mining systems, allowing for an easier and more efficient connection. Without being aware of the complexity of the process, this module enables the user to operate the system quickly and effectively. When a user sets a job or query and wants to see the results, this module works with the data mining system and displays the results.

GUI has three main components:

  • Legend: Visualisation results might require labels, colours, or icons, so a legend helps interpret the results. It is available at the bottom of the page. 

  • Status bar: A status bar makes it easier to see text information.

  • Toolbar: Every view has a toolbar that allows access to its essential features.

Knowledge base 

A knowledge base is essential for directing the search or assessing the significance of the pattern of results and is used throughout the data mining process. It also contains user opinions and experience data, which might be helpful throughout the data mining. 

The knowledge base provides inputs to the data mining engine to improve the accuracy and reliability of the outcome. The pattern assessment module regularly updates the knowledge base.

Learn data mining with Coursera

Any organisation that relies on data to drive decisions has its foundation in data mining systems. To successfully and effectively accomplish the challenging data mining process, each element of the architecture has to perform its specific set of tasks and require proper interaction with one another.

If you wish to learn more about data mining and its various techniques, components, and methods and gain industry-relevant skills, a data mining course such as the Data Mining Specialisation on Coursera can help you prepare a good foundation. 

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