Database Reference
In-Depth Information
Next, the list of frequent 2-itemsets is joined onto itself to form candidate
3-itemsets. For example {other vegetables,whole milk} and {whole
milk,rolls/buns} would be joined as {other vegetables,whole
milk,rolls/buns} . The algorithm retains those itemsets that satisfy the
minimum support. The following output shows that only two frequent 3-itemsets
have been identified.
itemsets <- apriori(Groceries, parameter=list(minlen=3,
maxlen=3,
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 3
maxlen target ext
3 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 … [2 set(s)] done [0.00s].
creating S4 object … done [0.00s].
The 3-itemsets are displayed next:
inspect(sort(itemsets, by ="support"))
items support
1 {root vegetables,
other vegetables,
whole milk} 0.02318251
2 {other vegetables,
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