rules <- apriori(GroceryDS, parameter = list(supp = 0.001, conf = 0.5, maxlen=3))
## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 0.5 0.1 1 none FALSE TRUE 5 0.001 1
## maxlen target ext
## 3 rules TRUE
##
## Algorithmic control:
## filter tree heap memopt load sort verbose
## 0.1 TRUE TRUE FALSE TRUE 2 TRUE
##
## Absolute minimum support count: 9
##
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[169 item(s), 9835 transaction(s)] done [0.00s].
## sorting and recoding items ... [157 item(s)] done [0.00s].
## creating transaction tree ... done [0.00s].
## checking subsets of size 1 2 3
## Warning in apriori(GroceryDS, parameter = list(supp = 0.001, conf = 0.5, :
## Mining stopped (maxlen reached). Only patterns up to a length of 3 returned!
## done [0.00s].
## writing ... [1472 rule(s)] done [0.00s].
## creating S4 object ... done [0.00s].
rules <- sort(rules, by="lift", decreasing=TRUE)
inspect(rules[1:10])
## lhs rhs support confidence coverage lift count
## [1] {Instant food products,
## soda} => {hamburger meat} 0.001220132 0.6315789 0.001931876 18.995654 12
## [2] {popcorn,
## soda} => {salty snack} 0.001220132 0.6315789 0.001931876 16.697793 12
## [3] {baking powder,
## flour} => {sugar} 0.001016777 0.5555556 0.001830198 16.408075 10
## [4] {ham,
## processed cheese} => {white bread} 0.001931876 0.6333333 0.003050330 15.045491 19
## [5] {Instant food products,
## whole milk} => {hamburger meat} 0.001525165 0.5000000 0.003050330 15.038226 15
## [6] {domestic eggs,
## processed cheese} => {white bread} 0.001118454 0.5238095 0.002135231 12.443639 11
## [7] {liquor,
## red/blush wine} => {bottled beer} 0.001931876 0.9047619 0.002135231 11.235269 19
## [8] {frozen vegetables,
## specialty chocolate} => {fruit/vegetable juice} 0.001016777 0.6250000 0.001626843 8.645394 10
## [9] {instant coffee,
## other vegetables} => {whipped/sour cream} 0.001016777 0.5263158 0.001931876 7.342292 10
## [10] {butter,
## hard cheese} => {whipped/sour cream} 0.002033554 0.5128205 0.003965430 7.154028 20
plot(rules, method="graph", engine = "igraph", layout = igraph::in_circle(), limit = 10)
forcluster<- GroceryDS[,itemFrequency(GroceryDS) > 0.06]
d_jaccard <- dissimilarity(forcluster, which = "items", method="affinity")
plot(hclust(d_jaccard, method = "ward.D2"), main = "Dendrogram of Grocery items")
