Data Mining Architecture

Data Mining: 

Data mining is described as the process of discovering or extracting interesting and meaningful knowledge from large volume of data which are stored in multiple data sources like databases, file system , data warehouse etc. The knowledge extracted from data mining contributes a lots of benefit to business strategies, scientific & medical research, governments and individual. Data warehouse systems has been designed to provide analytical reports that helps (DSS- Decision Support System) business users to make decisions.

Data Mining Architecture

Since data in stored in database or data warehouse so our data mining system should be designed in such a way that it can easily be coupled or decoupled from database or data warehouse system. There are 4 possible data mining architecture available which are described below in detail.

1. No-Coupling: 

  • In this architecture data mining system does not utilize any feature/functionality provide by database or data warehouse system.
  • Using this architecture data mining system retrieves data from a particular data source like file system, process data by applying various data mining algorithms to find pattern and then stores the result back into file system.
  • No-coupling is considered to be the most poor architecture of data mining.
  • It is used for very simple data mining process.

2. Loose Coupling:

  • In this architecture data mining system uses database or data warehouse system as a data source.
  • Data is retrieved from database or data warehouse,  data mining system apply data mining algorithms to process data and then stores the result back into database or warehouse.
  • This architecture is generally followed by memory based data mining system that doesn’t require high scalability and high performance.

3. Semi-tight coupling:

  • In this architecture data mining system uses database or data warehouse system as a data source.
  • Data is retrieved from database or data warehouse,  data mining system apply data mining algorithms to process data.
  • Data mining algorithms start using several functionality provided by database or data warehouse like sorting, indexing , aggregation etc
  • In this architecture some intermediate result can be stored into database or warehouse for better performance.

4. Tight Coupling

  • In this architecture data warehouse or database is treated as source of information using integration.
  • Data mining algorithms are applied to process the data.
  • All features/functionality of database or data warehouse is used by data mining algorithm to perform data mining task.
  • Its provides system scalability, very high performance and integrated information.

tight coupling-data-mining-architecture

There are three tiers in the tight-coupling data mining architecture:

1. Data layer:

  • Data layer can be a database and/or data warehouse systems.
  • This layer is an interface for all data sources.
  • Data mining results are stored in data layer so it can be presented to end-user in the form of reports or another kind of visualization.

2. Data mining application layer:

  • It is used to retrieve data from the database.
  • Some transformation routine can be performed here to transform data into the desired format.
  • Then data is processed using various data mining algorithms.

3. Front-end layer:

  • Provides intuitive and friendly user interface for end-user to interact with data mining system.
  • Data mining result presented in visualization form to the user in the front-end layer.

 

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