1.3 EFFECT OF MINSUP25002500FIGURE 9 SHOWS THE INFLUENCE OF MINSUP O...

7.1.3 Effect of minSup

2500

Figure 9 shows the influence of minSup on the runtime

2000

when using different densities. The runtime of Dynamic

Dynamic

directly correlates with the size of the dynamic computation

1500

matrix (Figure 5). A low minSup value leads to few matrix

1000Runtime [ms]

rows which need to be computed, while a high minSup value

500

Dynamic+P

leads to a slim row width (see Figure 5). The total number of

0

matrix cells to be computed is minSup ∗(|T | −minSup + 1),

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

with a maximum at minSup = ^|T|+1 ₂ . As long as the minSup

Minimum support

value is below the expected support value, the approach with

(b) Density = 0.5

(a) Density = 0.2

pruning shows similar characteristics; in this case, almost all

item(sets) are expected to be frequent. However, the speed-

up due to the pruning rapidly increases for minSup above

Figure 9: Runtime evaluation w.r.t. minSup.

this break-even point.

Management of Data (SIGMOD’94), Minneapolis, MN,