Classification of Data mining

 Classification of data mining

Data mining is the process of extracting valuable information from large amounts of raw data. It uses software tools to analyze patterns in big datasets. Since its development, data mining has become a key tool for researchers in research and development.

Businesses benefit greatly from data mining as it helps them understand customer needs and create strategies to increase profits. It also supports setting clear business goals and making better decisions.

Data mining relies on key elements like data collection, data storage (data warehousing), and computer processing. It uses mathematical algorithms to group data and predict the likelihood of future events.

Categorization based on the mined database: A data mining system can be grouped according to the types of databases it processes. These databases can be further divided based on specific principles such as data models, types of data, and other criteria, which aid in defining the categorization of a data mining system.

For instance, when categorizing a database by its data model, options include relational databases, transactional databases, object-relational databases, or data warehouse systems.

Categorization based on the type of knowledge mined:

1. Characterization
   Summarizing general characteristics of data in a dataset.

2. Discrimination
   Differentiating between classes or groups of data based on certain attributes.

3. Association and Correlation Analysis
   Identifying relationships or associations between variables within the data.

4. Classification
   Assigning data into predefined categories or classes based on known attributes.

5. Prediction
   Forecasting future values or trends based on historical data.

6. Outlier Analysis
   Detecting data points that deviate significantly from the general pattern.

7. Evolution Analysis
   Examining how data patterns change over time.

Categorization based on the techniques utilized:

Data mining systems can also be classified based on the techniques they use. These techniques can be evaluated based on the level of user interaction required or the methods of analysis applied.

Categorization based on Application Adapted:

Data mining systems can also be classified based on the applications they are used for. These include:

• Finance
• Telecommunications
• DNA analysis
• Stock markets
• Email filtering