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Resource-oriented approximation for frequent itemset mining from bursty data streams
2014
Proceedings of the 2014 ACM SIGMOD international conference on Management of data - SIGMOD '14
This study considers approximation techniques for frequent itemset mining from data streams (FIM-DS) under resource constraints. In FIM-DS, a challenging problem is handling a huge combinatorial number of entries (i.e., itemsets) to be generated from each streaming transaction and stored in memory. Various types of approximation methods have been proposed for FIM-DS. However, these methods require almost O(2 L ) space for the maximal length L of transactions. If some transaction contains sudden
doi:10.1145/2588555.2612171
dblp:conf/sigmod/YamamotoIF14
fatcat:7a3jjpsn7zfiden6msylmnuz5q