Applications of Knowledge Discovery

  • Katharina Morik
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3533)


Knowledge Discovery from Databases (KDD) – also named Data Mining – is a growing field since 10 years which combines techniques from databases, statistics, and machine learning. Applications of KDD most often have one of the following goals:

– Customer relationship management: who are the best customers, which products are to be offered to which customers (direct marketing or customer acquisition), which customers are likely to end the relationship (customer churn), which customers are likely to not pay (also coined as fraud detection)?

– Decision support applies to almost all areas, ranging from medicine over marketing to logistics. KDD applications aim at a data-driven justification of decisions by relating actions and outcomes.

– Recommender systems rank objects according to user profiles. The objects can be, for instance, products as in the amazon internet shop, or documents as in learning search engines. KDD applications do not assume user profiles to be given but learns tehm from observations of user behavior.

– Plant asset management moves beyond job scheduling and quality control. The goal is to optimize the overall benefits of production.


Knowledge Discovery Recommender System Customer Relationship Management Concept Drift Audio Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Katharina Morik
    • 1
  1. 1.Computer Science Department, LS VIIIUniv. Dortmund 

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