Data Mining

1. Characteristics and Objectives

1.1. The data mining environment usually has a Client Server Architecture

1.2. Data mining produces five types of information:

1.2.1. Associations

1.2.2. Sequences

1.2.3. Classifications

1.2.4. Groupings

1.2.5. Forecasts

1.3. They use various tools and techniques

1.4. Data mining tools are easily combined and can be quickly analyzed and processed

1.5. Due to the large amount of data, it is sometimes necessary to use parallel processing for data mining

2. Protocol of a data mining project

2.1. Understanding of the business and the problem to be solved

2.2. Determination, obtaining and cleaning of the necessary data

2.3. Creation of mathematical models

2.4. Validation, communication, etc. of the results obtained

2.5. Integration, if applicable, of the results in a transactional system or similar

3. Some Techniques

3.1. Neural Networks

3.2. Linear regression

3.3. Clustering or Clustering

3.4. Time series

3.5. Expert systems

3.6. Intelligent Systems

4. What's it?

4.1. Techniques aimed at extracting actionable knowledge.

5. Who consolidated the terms?

5.1. Rakesh Agrawal

5.2. Gio Wiederhold