Classification of Data Mining Systems

Data mining refers to the process of extracting important data from raw data. It analyses the data patterns in huge sets of data with the help of several software. Ever since the development of data mining, it is being incorporated by researchers in the research and development field.

With Data mining, businesses are found to gain more profit. It has not only helped in understanding customer demand but also in developing effective strategies to enforce overall business turnover. It has helped in determining business objectives for making clear decisions.

Data collection and data warehousing, and computer processing are some of the strongest pillars of data mining. Data mining utilizes the concept of mathematical algorithms to segment the data and assess the possibility of occurrence of future events.

To understand the system and meet the desired requirements, data mining can be classified into the following systems:

• Classification based on the mined Databases
• Classification based on the type of mined knowledge
• Classification based on statistics
• Classification based on Machine Learning
• Classification based on visualization
• Classification based on Information Science
• Classification based on utilized techniques
• Classification based on adapted applications

Classification Based on the mined Databases

A data mining system can be classified based on the types of databases that have been mined. A database system can be further segmented based on distinct principles, such as data models, types of data, etc., which further assist in classifying a data mining system.

For example, if we want to classify a database based on the data model, we need to select either relational, transactional, object-relational or data warehouse mining systems.

Classification Based on the type of Knowledge Mined

A data mining system categorized based on the kind of knowledge mind may have the following functionalities:

1. Characterization
2. Discrimination
3. Association and Correlation Analysis
4. Classification
5. Prediction
6. Outlier Analysis
7. Evolution Analysis

Classification Based on the Techniques Utilized

A data mining system can also be classified based on the type of techniques that are being incorporated. These techniques can be assessed based on the involvement of user interaction involved or the methods of analysis employed.

Classification Based on the Applications Adapted

Data mining systems classified based on adapted applications adapted are as follows:

1. Finance
2. Telecommunications
3. DNA
4. Stock Markets
5. E-mail

Examples of Classification Task

Following is some of the main examples of classification tasks:

• Classification helps in determining tumor cells as benign or malignant.
• Classification of credit card transactions as fraudulent or legitimate.
• Classification of secondary structures of protein as alpha-helix, beta-sheet, or random coil.
• Classification of news stories into distinct categories such as finance, weather, entertainment, sports, etc.

Integration schemes of Database and Data warehouse systems

No Coupling

In no coupling schema, the data mining system does not use any database or data warehouse system functions.

Loose Coupling

In loose coupling, data mining utilizes some of the database or data warehouse system functionalities. It mainly fetches the data from the data repository managed by these systems and then performs data mining. The results are kept either in the file or any designated place in the database or data warehouse.

Semi-Tight Coupling

In semi-tight coupling, data mining is linked to either the DB or DW system and provides an efficient implementation of data mining primitives within the database.

Tight Coupling

A data mining system can be effortlessly combined with a database or data warehouse system in tight coupling.