Maximum entropy based significance of itemsets

  title={Maximum entropy based significance of itemsets},
  author={Nikolaj Tatti},
  journal={Seventh IEEE International Conference on Data Mining (ICDM 2007)},
We consider the problem of defining the significance of an itemset. We say that the itemset is significant if we are surprised by its frequency when compared to the frequencies of its sub-itemsets. In other words, we estimate the frequency of the itemset from the frequencies of its sub-itemsets and compute the deviation between the real value and the estimate. For the estimation we use Maximum Entropy and for measuring the deviation we use Kullback–Leibler divergence. A major advantage compared… CONTINUE READING
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