arules

I have a dataset which looks like this: "user.get","search_restaurants","cuisines.get" "user.get","search_restaurants","user.get","search_restaurants" "order/address/get_user_addresses" "search_restaurants","search_restaurantssearch_restaurants" "restaurant.get","search_restaurants","order/menu","restaurant.get","restaurant.get","restaurant.get","order/menu","order/menu","restaurant.get","restaurant.getsearch_restaurantsrestaurant.get","user.get","order/menu","order/menu","get_user_reviews_filtered","order/menu","restaurant.get" When I run the apriori algorithm on it: txn1 = read.transactions

Assume we have 3 rules: [1] {A,B,D} -> {C} [2] {A,B} -> {C} [3] Whatever it is Rule [2] is a subset of rule [1] (because rule [1] contains all the items in rule [2] ), so rule [1] should be eliminated (because rule [1] is too specific and its information is included in rule [2] ) I searched through the internet and everyone is using these code to remove redundant rules: subset.matrix <- is.subset(rules.sorted, rules.sorted) subset.matrix[lower.tri(subset.matrix, diag=T)] <- NA redundant <- colSums(subset.matrix, na.rm=T) >= 1 which(redundant) rules.pruned <- rules.sorted[!redundant] I dont

I am trying to find association rules using the apriori function from arules package in R. rules <- apriori(data=data, parameter=list(supp=0.001,conf = 0.08), appearance = list(default="lhs",rhs="YOGHURT"), control = list(verbose=F)) rules <- sort(rules, decreasing=TRUE,by="confidence") inspect(rules[1:3]) lhs rhs support confidence lift 1. {A,B} {C} 0.04 0.96 0.25 2. {C,A} {D} 0.05 0.95 0.26 3. {B,D} {A,C} 0.03 0.93 0.24 With the code showed above I got some association rules saved in the variable "rules" ordered by confidence in a decreasing way. But I would like to order these rules by

Ok, so I have searched a lot and want to run arules on sales data. I just need to properly get the data in the right format and set up with the correct "factors" or "variables" and in basket form. Right now I have sales data with the Order# and then the items inside that. Each order is unique (every new order, a new # gets created and includes the part#), but the same items obviously can appear in many orders. Currently, my data is set up like this: Order# Part# PartDescription 1 A PartA 1 B PartB 1 G PartG 2 R PartR 3 A PartA 3 B PartB 4 E PartE 5 Y PartY 6 A PartA 6 B PartB 6 F PartF 6 V

I read data from a csv file, the data has 3 columns, one is transaction id, the other two are product and product catagory. I need to convert this into transactions in order to use the apriori function in arules . It shows an error when I convert to transactions: dat <- read.csv("spss.csv",head=TRUE,sep="," , as.is = T) dat[,2] <- factor(dat[,2]) dat[,3] <- factor(dat[,3]) spssdat <- dat[,c(1,2,3)] str(spssdat) 'data.frame': 108919 obs. of 3 variables: $ Transaction_id: int 3000312 3000312 3001972 3003361 3003361 3003361 3003361 3003361 3003361 3004637 ... $ product_catalog : Factor w/ 9