Data Mining

Data mining is systematic applying of statistically and mathematically verified methods on a dataset to recognize new patterns. It is commonly used with the large data volumes, for which the methods with asymptotic running time applied.
If the modeling conditions for the data generation are not fixed, there can be various areas of application for small and middle data volumes. In practice, particularly in German, the anglo-saxon term “data mining” was established for the entire process known as “Knowledge Discovery in Databases”.
In data mining a model describes the relations between input data (“explanatory variable”) and output data (“target variables”). Models are used for to the forecast or description of phenomena.
Predictive data mining is used by companies in terms of Business Intelligence (BI) and/or Business Analytics projects, particularly to make forecasts. Chances, risks or scores are determined.

Possible areas of application:

  • analytical CRM
    • Market basket analysis
    • Recognition of cross and up-selling-potentials
      Churn Management
      • Customer attrition
      • Customer value modelling
      • Customer segmentation
  • Credit rating
  • Fraud detection
  • Process monitoring
  • Namechecking

In data mining diverse mathematically and statistically based procedures are applied:

  • Classification and segmentation
  • Link analysis
  • Clustering analysis
  • Procedure for the recognition of changes and deviations
  • Time series analysis

The Prospero’s data mining  engine is based on an innovative meta modelling algorithm. It enables the fully automatic building of rating and scoring models.

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