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Model Evaluation

Data Mining

Data mining aims to analyze data from different angles, categorize data, and summarize identified relationships. Technically speaking, data mining is the process of finding correlations or patterns in data.

It relies on complex and sophisticated algorithms to segment data and evaluate future probabilities. Data mining is also known as "knowledge discovery" in data circles.

There are five methods:

  1. Association - looking for patterns in which an event is linked to another one
  2. Sequence analysis - looking for patterns in which one event leads to another one at a later time
  3. Classification - looking for new patterns, or even changing the way data is organized
  4. Clustering - finding and visually documenting previously unknown fact groups
  5. Prediction - discovering patterns of data that can lead to reasonable predictions. This is also known as predictive analytics

 

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