Data mining (1)

• Data mining is more clear to be called “knowledge mining from data”. Knowledge extraction, data/pattern analysis.
• The knowledge extraction/discovery is a sequence of:
  1. Data cleaning (remove noise and inconsistent data)
  2. Data integration (from multi sources)
  3. Data selection
  4. Data transformation ( to specific form)
  5. Data mining (extract data pattern)
  6. Patter evaluation
  7. Knowledge presentation
• Classification is the process of finding a model/function that describes and distinguishes data classes/concepts, for the purpose of being able too use the model to predict the class of objects whose class label is unknown. The derived model is based on the analysis of a set of training data whose class label is known.
• Cluster analysis. Unlike classification and prediction, which analyze class-labeled data, clustering analyzes data objects without knowing a known class label.
• Data preparing, such as data normalization.