Maximum entropy based significance of itemsets

@article{Tatti2007MaximumEB,
  title={Maximum entropy based significance of itemsets},
  author={Nikolaj Tatti},
  journal={Seventh IEEE International Conference on Data Mining (ICDM 2007)},
  year={2007},
  pages={312-321}
}
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
Highly Cited
This paper has 65 citations. REVIEW CITATIONS

Citations

Publications citing this paper.

65 Citations

01020'09'12'15'18
Citations per Year
Semantic Scholar estimates that this publication has 65 citations based on the available data.

See our FAQ for additional information.

References

Publications referenced by this paper.

Similar Papers

Loading similar papers…