Data Mining Methods
Data mining is used to analyze existing data sets. Statistical methods and algorithms are utilized to identify new trends, patterns, and relationships. For example, data mining enables the recognition and evaluation of the purchasing behavior of certain customer groups.
Data mining and Predictive Analytics (P. A.) are often used synonymously. In fact, methods and tools of data mining also play a crucial role in the P.A. process. However, predictive analytics goes beyond data mining and employs additional methods such as machine learning, elements of game theory, or simulation techniques.
Classic Data Mining Methods include:
- Clustering: This involves segmenting data and forming different groups (for example, customers by income levels).
- Classification: In this case, the groups/classes are predefined. Data elements are automatically assigned to different classes (e.g., high-sales and low-sales branches).
- Regression Analysis: Identifying relationships between (multiple) dependent and independent variables (For example, does product sales depend on product price and customer income?).
- Association Analysis: Searching for patterns where one event is associated with another event; dependencies between datasets are described by if-then rules (for example, if a customer buys cola, they also buy pretzels).
Data Mining – Machine Learning
Data mining also uses neural networks, which resemble the workings of the human brain and learn existing structures or patterns through numerous data cycles. Therefore, data mining is closely related to machine learning, meaning applications and methods in which computer programs autonomously acquire new knowledge. While data mining focuses on finding new patterns that already exist in the data, predictive methods aim to derive new calculation functions from existing data. In this case, algorithms are trained to learn from the available data, autonomously create a data model, and use it for predictions or decisions. An example of this is the suggestions on amazon.com “You may also like”.
Your Contact for Data Mining
Our data specialist Michael Eicks (eicks@trebbau.com, Tel.: 0221/37646 – 558) is happy to assist you in differentiating between the methods and showing you the opportunities that data mining and predictive analytics offer.