Data Mining Architecture in Data Mining Systems

Data mining is a complex process and involves a number of components that constitute to form data mining architecture of a data mining system.

In this article, we will discuss in detail data mining architecture. But before we explore data mining architecture, let’s go through the topics listed under the table of content that we will cover in this article.

Table of content (TOC)

• Process Flow of Data Mining Architecture
• Components of Data Mining Architecture

Read more about data mining 

Process Flow of Data Mining Architecture

The above diagram depicts the components of data mining architecture, which explains the processes as follows –

• The user inputs the requests
• The requests are sent to data mining engines for pattern evaluation
• The system tries to seek a solution to the query using the existing databases and thus generates the metadata
• The metadata is sent to the data mining engine for analysis, which may interact with pattern evaluation modules to find the results
• The obtained result is ready to be interpreted at the front end via a suitable interface

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Components of Data Mining Architecture

The architecture of data mining comprises the below components –

Data Sources

For a successful data mining process, you need data and the source of data can be anything. It can be a worldwide web, database, data warehouse, or other data repositories that can provide data in multiple forms like –

• Microsoft Excel spreadsheets
• Image, video, and audio files
• Text files
• Data from the internet
• User-generated data
• Datetime
• Timestamp
• Biometric data

The data collected from different sources need to be cleaned and integrated before moving on to the next stage.

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Database/Data Warehouse Server

The database/data warehouse server is the crucial element of the architecture of data mining that contains the cleaned and integrated data, under a unified schema, and is ready to be processed. The database/data warehouse server retrieves the relevant data basis the user request.

Data Mining Engine

Data Mining Engine comprises modules or tools for performing various tasks like data clustering, data classification, prediction, and correlation analysis, on the data stored in Database/data warehouse server. These sets of tools include –

• An interpreter to transmit commands to the computer
• Gear between the engine and the data warehouse to produce and handle bidirectional communication
• A set of data mining algorithms

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Pattern Evaluation

The pattern evaluation module investigates a pattern using a threshold value. It works in collaboration data mining engine and uses stake measures to find interesting and useful patterns. Pattern evaluation may also coordinate with the mining module, basis data mining techniques. It is suggested to push the evaluation of pattern stakes as much as possible into the mining procedure to find the desirable patterns and ensure an effective data mining process.

Graphical User Interface

GUI serves as the link between the user and the data mining system. GUI hides the complex process of data interpretation and presents the data in an easy and readable format. The main component of a GUI are –

Legend

Some visualization results need colors, icons, or labels. A legend at the bottom of the visualizer page helps to interpret the results

Status bar

The status bar facilitates the visualization of textual information

Toolbar

Every view provides a specific toolbar to access the crucial features of the view

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Knowledge Base

A knowledge base is defined as the repository of domain-specific or general knowledge gathered from data sources. It stores large amounts of organized data and follows a defined schema or “data model” that facilitates its storage, retrieval, and modification and is powered by artificial intelligence and machine learning algorithms. Knowledge Base gives inputs to the data mining engine and helps in pattern evaluation.

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Data mining systems form the backbone of any data drive organization to facilitate their decision-making processes. Every component of the data mining architecture has a defined set of tasks to perform and leads to an efficient and successful data mining process.

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