Prof. Dr.

Wrobel, Stefan

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Support Estimation in Frequent Itemset Mining by Locality Sensitive Hashing

( 2019)
Pick, Annika
;
Horvath, Tamas
;
Wrobel, Stefan

The main computational effort in generating all frequent itemsets in a transactional database is in the step of deciding whether an itemset is frequent, or not. We present a method for estimating itemset supports with two-sided error. In a preprocessing step our algorithm first partitions the database into groups of similar transactions by using locality sensitive hashing and calculates a summary for each of these groups. The support of a query itemset is then estimated by means of these summaries. Our preliminary empirical results indicate that the proposed method results in a speed-up of up to a factor of 50 on large datasets. The F-measure of the output patterns varies between 0.83 and 0.99.