Specifying Mining Algorithms with Iterative User-Defined Aggregates: A Case Study

  • Fosca Giannotti
  • Giuseppe Manco
  • Franco Turini
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2168)


We present a way of exploiting domain knowledge in the design and implementation of data mining algorithms, with special attention to frequent patterns discovery, within a deductive framework. In our framework domain knowledge is represented by deductive rules, and data mining algorithms are constructed by means of iterative user-defined aggregates. Iterative user-defined aggregates have a fixed scheme that allows the modularization of data mining algorithms, thus providing a way to exploit domain knowledge in the right point. As a case study, the paper presents user-defined aggregates for specifying a version of the apriori algorithm. Some performance analyses and comparisons are discussed in order to show the effectiveness of the approach.


Association Rule Mining Algorithm Frequent Itemsets Inductive Rule Data Mining Algorithm 
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 2001

Authors and Affiliations

  • Fosca Giannotti
    • 1
  • Giuseppe Manco
    • 1
  • Franco Turini
    • 2
  1. 1.CNUCE-CNRPisaItaly
  2. 2.Department of Computer SciencePisaItaly

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