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Support Estimation in Frequent Itemset Mining by Locality Sensitive Hashing
2019
Lernen, Wissen, Daten, Analysen
The main computational eort 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 rst 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.
dblp:conf/lwa/Pick0W19
fatcat:aka6cmx6ejhdlibjiqwqpa2u5a