Quality Measures in Pattern Mining

  • Sebastián Ventura
  • José María Luna


In this chapter different quality measures to evaluate the interest of the patterns discovered in the mining process are described. Patterns represent major features of data so their interestingness should be accordingly quantified by considering metrics that determine how representative a specific pattern is for the dataset. Nevertheless, a pattern can also be of interest for a user despite the fact that this pattern does not describe useful and intrinsic properties of data. Thus, any quality measure can be divided into two main groups: objective and subjective quality measures. Whereas objective measures describe statistical properties of data, subjective quality measures take into account both the data properties and external knowledge provided by the expert in the application domain.


National Quality Measures Pattern Mining Original Contingency Table Objective Interestingness Measures Association Rules 
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 International Publishing Switzerland 2016

Authors and Affiliations

  • Sebastián Ventura
    • 1
  • José María Luna
    • 1
  1. 1.Department of Computer Science and Numerical AnalysisUniversity of CordobaCordobaSpain

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