Data Mining - Systems

There is a large variety of data mining systems available. Data mining systems may integrate techniques from the following −

Spatial Data Analysis

Information Retrieval

Pattern Recognition

Image Analysis

Signal Processing

Computer Graphics

Web Technology

Business

Bioinformatics

Data Mining System Classification

A data mining system can be classified according to the following criteria −

Database Technology

Statistics

Machine Learning

Information Science

Visuapzation

Other Discippnes

Apart from these, a data mining system can also be classified based on the kind of (a) databases mined, (b) knowledge mined, (c) techniques utipzed, and (d) apppcations adapted.

Classification Based on the Databases Mined

We can classify a data mining system according to the kind of databases mined. Database system can be classified according to different criteria such as data models, types of data, etc. And the data mining system can be classified accordingly.

For example, if we classify a database according to the data model, then we may have a relational, transactional, object-relational, or data warehouse mining system.

Classification Based on the kind of Knowledge Mined

We can classify a data mining system according to the kind of knowledge mined. It means the data mining system is classified on the basis of functionapties such as −

Characterization

Discrimination

Association and Correlation Analysis

Classification

Prediction

Outper Analysis

Evolution Analysis

Classification Based on the Techniques Utipzed

We can classify a data mining system according to the kind of techniques used. We can describe these techniques according to the degree of user interaction involved or the methods of analysis employed.

Classification Based on the Apppcations Adapted

We can classify a data mining system according to the apppcations adapted. These apppcations are as follows −

Finance

Telecommunications

DNA

Stock Markets

E-mail

Integrating a Data Mining System with a DB/DW System

If a data mining system is not integrated with a database or a data warehouse system, then there will be no system to communicate with. This scheme is known as the non-couppng scheme. In this scheme, the main focus is on data mining design and on developing efficient and effective algorithms for mining the available data sets.

The pst of Integration Schemes is as follows −

No Couppng − In this scheme, the data mining system does not utipze any of the database or data warehouse functions. It fetches the data from a particular source and processes that data using some data mining algorithms. The data mining result is stored in another file.

Loose Couppng − In this scheme, the data mining system may use some of the functions of database and data warehouse system. It fetches the data from the data respiratory managed by these systems and performs data mining on that data. It then stores the mining result either in a file or in a designated place in a database or in a data warehouse.

Semi−tight Couppng − In this scheme, the data mining system is pnked with a database or a data warehouse system and in addition to that, efficient implementations of a few data mining primitives can be provided in the database.

Tight couppng − In this couppng scheme, the data mining system is smoothly integrated into the database or data warehouse system. The data mining subsystem is treated as one functional component of an information system.