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Formally, an up-to-date pattern is a pair with the itemset and its valid corresponding lifetime in which the user-defined minimum support threshold must be satisfied.
When a database has a large number of items (from thousands to hundreds of thousands) but the number of transactions is small, these algorithms cannot run when the minimum support threshold is also small (because the search space is huge).
The minimum support threshold, [9 values from 5 to 0.01%].
After realizing the positive outcome of changing minimum support threshold, it was decreased to 1percentt.
We tried nine different values for the minimum support threshold, from 5 to 0.01%.
Minimum support threshold for choosing maximal frequent itemsets have been changed from 3to1%1%.
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Extensive experiments on real-life and synthetic databases show that FMaxSM, FGenCloSM and MaxGenCloSM are up to two orders of magnitude faster than the state-of-the-art algorithms and that the proposed algorithms consume much less memory, especially for low minimum support thresholds and for dense databases.
For other related results, a constraint, named maximum constraint, is used by [19] to discover association rules with many minimum support thresholds.
Minimum support ith varying minimum support thresholds, from 0.02 to 0.0001, the best performing solution is stably with (f=max) (Fig. 6b) and (m=) confidence, and indifferently max or min as g.
The experimental results on PA10F with different minimum support thresholds are shown in Figure 4.
The input of the algorithm is uncertain big data, user-specified minimum support (minsup) threshold, and the SAM constraints.
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minimum operating threshold
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