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Data Mining

Data Mining automatically sifts through data to find hidden patterns, discover new insights, and make predictions.

Data Mining can provide valuable results:

• Predict customer behavior (Classification)

  Classification is a data mining function that assigns items in a collection to target categories or classes. For example:

  • Telecom Churner or non-Churner
  • Abnormal behavior (fraud) or NOT
  
  
• Predict or estimate a value (Regression)

  Regression is a data mining function that predicts a number. For example: profit and loss.

• Segment a population (Clustering)

  Clustering analysis finds clusters of data objects that are similar in some sense to one another.

• Identify factors more associated with a business problem (Attribute Importance)
• Find profiles of targeted people or items (Decision Trees)

  Decision Trees rules provide model transparency so that a business user, marketing analyst, or business analyst can understand the basis of the model's predictions, and therefore, be comfortable acting on them and explaining them to others .

• Determine important relationships and “market baskets” within the population (Associations)
• Find fraudulent or “rare events” (Anomaly Detection)

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