Database Reference
In-Depth Information
whole milk,
yogurt} 0.02226741
In the next iteration, there is only one candidate 4-itemset
{root
vegetables,other vegetables,whole milk,yogurt}
, and its support is
below 0.02. No frequent 4-itemsets have been found, and the algorithm converges.
itemsets <- apriori(Groceries, parameter=list(minlen=4,
maxlen=4,
support=0.02, target="frequent itemsets"))
parameter specification:
confidence minval smax arem aval originalSupport support
minlen
0.8 0.1 1 none FALSE TRUE 0.02 4
maxlen target ext
4 frequent itemsets FALSE
algorithmic control:
filter tree heap memopt load sort verbose
0.1 TRUE TRUE FALSE TRUE 2 TRUE
apriori - find association rules with the apriori algorithm
version 4.21 (2004.05.09) (c) 1996-2004 Christian Borgelt
set item appearances …[0 item(s)] done [0.00s].
set transactions …[169 item(s), 9835 transaction(s)] done
[0.00s].
sorting and recoding items … [59 item(s)] done [0.00s].
creating transaction tree … done [0.00s].
checking subsets of size 1 2 3 done [0.00s].
writing … [0 set(s)] done [0.00s].
creating S4 object … done [0.00s].
The previous steps simulate the Apriori algorithm at each iteration. For the
Groceries
dataset, the iterations run out of support when
k
= 4. Therefore, the
frequent itemsets contain 59 frequent 1-itemsets, 61 frequent 2-itemsets, and 2
frequent 3-itemsets.
When the
maxlen
parameter is not set, the algorithm continues each iteration
until it runs out of support or until k reaches the default
maxlen=10
. As shown
in the code output that follows, 122 frequent itemsets have been identified. This
