{"id":7963,"date":"2018-02-13T13:09:50","date_gmt":"2018-02-13T13:09:50","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=7963"},"modified":"2021-05-28T14:07:50","modified_gmt":"2021-05-28T08:37:50","slug":"data-mining-architecture","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/data-mining-architecture\/","title":{"rendered":"Data Mining Architecture – Data Mining Types and Techniques"},"content":{"rendered":"

In this Data mining Tutorial<\/strong>, we will study Data Mining Architecture. Also, will learn types of Data Mining Architecture, and Data Mining techniques with required technologies drivers.<\/p>\n

So, let’s start\u00a0the Architecture of Data Mining.<\/p>\n

What is Data Mining?<\/h3>\n

We can say it is a process of extracting interesting knowledge from large amounts of data. That is stored<\/span> in many data sources. Such as file systems, databases, data warehouses. Also, knowledge used to contributes a lot of benefits to business and individual.<\/p>\n

Data Mining Architecture<\/h3>\n

\""<yoastmark\"<\/a><\/p>\n

Data mining Architecture system contains too many components. That is a data source, data warehouse server, data mining engine, and knowledge base.<\/p>\n

a. Data Sources<\/h4>\n

There are so many documents present. That is a database, data warehouse, World Wide Web (WWW). That are the actual sources of data. Sometimes, data may reside<\/span> even in plain text files or spreadsheets. World Wide Web or the Internet is another big source of data.<\/p>\n

b. Database or Data Warehouse Server<\/h4>\n

The database server contains the actual data that is ready to be processed<\/span>. Hence, the server handles retrieving the relevant data. That is based <\/span>on the data mining request of the user.<\/p>\n

c. Data Mining Engine<\/h4>\n

In data mining system data mining engine is the core component. As It consists a number of<\/span> modules. That we used to perform data mining tasks. That includes association, classification, characterization, clustering, prediction, etc.<\/p>\n

d. Pattern Evaluation Modules<\/h4>\n

This module is mainly<\/span> responsible for the measure of interestingness of the pattern. For this, we use a threshold value. Also, it interacts with the data mining engine. That\u2019s main focus is to search towards interesting patterns.\u00a0<\/strong><\/p>\n

e. Graphical User Interface<\/h4>\n

We use this interface to communicate between the user and the data mining system. Also, this module helps the user use the system easily<\/span> and efficiently<\/span>. They don\u2019t know the real complexity of the process.<\/p>\n

When the user specifies a query, this module interacts with the data mining system. Thus, displays the result in an easily<\/span> understandable manner.<\/p>\n

f. Knowledge Base<\/h4>\n

In whole data mining process, the knowledge base is beneficial. We use it to guiding the search for the result patterns. The knowledge base might even contain user beliefs and data from user experiences. That can be useful in the process of<\/span> data mining.<\/p>\n

The data mining engine might get inputs from the knowledge. That is the base to make the result more accurate and reliable. The pattern evaluation module interacts with the knowledge base. That is on a regular basis to get inputs and also to update it.<\/p>\n

Types of Data Mining Architecture<\/h3>\n

Data is collected<\/span> through business transactions and stored in relational database systems. Also, these business processes have been built<\/span> to provide analytical reports. That is for business users to make decisions. As also data is now stored in database or data warehouse system? So data mining system should be designed<\/span> to decouple.<\/p>\n

This question leads to four possible architectures:<\/em><\/p>\n

a. No-coupling Data Mining<\/h4>\n

In this architecture, data mining system does not use any functionality of a database. A no-coupling data mining system retrieves data from a particular data sources.<\/p>\n

The no-coupling data mining architecture does not take any advantages of a database. That is already very efficient in organizing, storing, accessing and retrieving data. The no-coupling architecture is considered<\/span> a poor architecture for data mining system. But it is used<\/span> for simple data mining processes.<\/p>\n

b. Loose Coupling Data Mining<\/h4>\n

In this architecture, data mining system uses a database for data retrieval. In loose coupling, data mining architecture, data mining system retrieves data from a database. And it stores the result in those systems.<\/p>\n

Data mining architecture is for memory-based data mining system. That does not must high scalability and high performance.<\/p>\n

c. Semi-Tight Coupling\u00a0Data Mining<\/h4>\n

In semi-tight coupling, data mining system uses several features of data warehouse systems. That is to perform some data mining tasks. That includes sorting, indexing, aggregation. In this, some intermediate result can be stored<\/span> in a database for better performance.<\/p>\n

d. Tight Coupling Data Mining<\/h4>\n

In tight coupling, a data warehouse is treated<\/span> as an information retrieval component. All the features of database or data warehouse are used<\/span> to perform data mining tasks. This architecture provides system scalability, high performance, and integrated information.<\/p>\n

There are three tiers in the tight-coupling data mining architecture:<\/em><\/p>\n

i. Data Layer<\/h4>\n

We can define data layer as a database or data warehouse systems. This layer is an interface for all data sources. Data mining results are stored<\/span> in the data layer. Thus, we can present to end-user in form of reports or another kind of visualization.<\/p>\n

ii. Data mining application layer<\/h4>\n

It is to retrieve data from a database. Some transformation routine has to perform<\/span> here. That is to transform data into the desired format. Then we have to process data<\/span> using various data mining algorithms.<\/p>\n

iii. Front-end layer<\/h4>\n

It provides the intuitive and friendly user interface for end-user. That is to interact with data mining system. Data mining result presented in visualization form to the user in the front-end layer.<\/p>\n

Data Mining Techniques<\/h3>\n

There are several data mining techniques present, mentioned below<\/p>\n

\""<yoastmark\"<\/a><\/p>\n

a. Decision Trees<\/strong><\/p>\n

It\u2019s the most common technique, we use for data mining. As because of its simplest structure. The root of decision tree act as a condition. Each answer leads to specific data that help us to determine final decision based upon it.<\/p>\n

b. Sequential Patterns<\/strong><\/p>\n

As we use this to discover regular events, similar patterns in transaction data. The historical data of customers helps us to identify the past transactions in a year.<\/p>\n

c. Clustering<\/strong><\/p>\n

Having similar characteristics clusters objects have to form<\/span>, by using automatic method. We use clustering, to define classes. Then suitable objects have to place <\/span>in each class.<\/p>\n

d. Prediction<\/strong><\/p>\n

We use this method defines the relationship between independent and dependent instances.<\/p>\n

e. Association<\/strong><\/p>\n

It is also known as relation technique. Also, in this, we have to recognize a pattern. That it is based<\/span> upon the relationship of items in a single transaction.<\/p>\n

Also, we can suggest the technique for market basket analysis. That is to explore the products that customer frequently<\/span> demands.<\/p>\n

f. Classification<\/strong><\/p>\n

This is based<\/span> on machine learning. We use this to classify each item in a particular set into predefined groups. Although, this method adopts mathematical techniques. Such as neural networks, linear programming, and decision trees and so on.<\/p>\n

Required Technological Drivers<\/h3>\n

As data mining applications<\/strong> are present for all size machines. Such as mainframe, workstations, clouds, client, and server. The size of enterprise applications varies from 10 Gb to 100 Tb. NCR systems are preferring for\u00a0<\/span>deliver the applications exceeding 100 Tb. The technological drivers are as<\/p>\n

a. Database size<\/strong><\/p>\n

As for maintaining and processing the huge amount of data, we need powerful systems.<\/p>\n

b. Query Complexity<\/strong><\/p>\n

To analyze the complex and large number of queries, we need a more powerful system.<\/p>\n

Conclusion<\/h3>\n

As a result, we have studied Data Mining Architecture. Also, learned it’s one of the types. Along with this, we have studied it’s techniques also. Furthermore, if you feel any query feel free to ask in a comment section.<\/p>\n","protected":false},"excerpt":{"rendered":"

In this Data mining Tutorial, we will study Data Mining Architecture. Also, will learn types of Data Mining Architecture, and Data Mining techniques with required technologies drivers. So, let’s start\u00a0the Architecture of Data Mining....<\/p>\n","protected":false},"author":6,"featured_media":8298,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[18],"tags":[3342,3369,9098,11094,12720,12733,14720,15039,15684],"class_list":["post-7963","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-mining","tag-data-mining-architecture","tag-data-mining-techniques","tag-no-coupling-data-mining","tag-query-complexity","tag-semi-tight-coupling-data-mining","tag-sequential-patterns","tag-tight-coupling-data-mining","tag-types-of-data-mining-architecture","tag-what-is-data-mining"],"yoast_head":"\nData Mining Architecture - Data Mining Types and Techniques - DataFlair<\/title>\n<meta name=\"description\" content=\"Data Mining Architecture- What is Data Mining,Types of Data Mining Architecture, no-coupling, Tight Coupling, Semi-tight Coupling, loss coupling Data Mining\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/data-flair.training\/blogs\/data-mining-architecture\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Data Mining Architecture - 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