MINI: Mining Informative Non-redundant Itemsets

  • Arianna Gallo
  • Tijl De Bie
  • Nello Cristianini
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4702)


Frequent itemset mining assists the data mining practitioner in searching for strongly associated items (and transactions) in large transaction databases. Since the number of frequent itemsets is usually extremely large and unmanageable for a human user, recent works have sought to define condensed representations of them, e.g. closed or maximal frequent itemsets. We argue that not only these methods often still fall short in sufficiently reducing of the output size, but they also output many redundant itemsets. In this paper we propose a philosophically new approach that resolves both these issues in a computationally tractable way. We present and empirically validate a statistically founded approach called MINI, to compress the set of frequent itemsets down to a list of informative and non-redundant itemsets.


Null Model Frequent Itemsets Support Threshold Interestingness Measure Condensed Representation 
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 2007

Authors and Affiliations

  • Arianna Gallo
    • 1
  • Tijl De Bie
    • 1
  • Nello Cristianini
    • 1
    • 2
  1. 1.University of Bristol, Department of Engineering MathematicsUK
  2. 2.University of Bristol, Department of Computer ScienceUK

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