I am trying to create a association using apriori algorithm.The data contains around 33000 records.Below is the sample of the dataid  code1   191   581   1112   192   1112   1673   123   793   854   965   196   587   127   187   407   487   857   867   135In R this data is:structure(list(id = c(1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 5, 6, 7, 7, 7, 7, 7, 7, 7), code = c(19, 58, 111, 19, 111, 167, 12, 79, 85, 96, 19, 58, 12, 18, 40, 48, 85, 86, 135)), .Names = c(id, code), row.names = c(NA, -19L), class = data.frame)Using the following code I tried to build the association# creating a string of codes based on the idlibrary(dplyr)Asso2 = Asso %>%   group_by(patient_id) %>%   summarise(hi = toString(hcc_id))# converting into transactionslibrary(arules)fact <- data.frame(lapply(Asso2,as.factor))trans <- as(fact, 'transactions') # Applying apriorrules = apriori(trans, parameter = list(supp = 0.001, conf = 0.001,target = rules))rulesinspect(rules)I am getting totally 96 rules like below with empty lhs and I trying to understand whether we cannot make any rules from this data or am I missing anything here. Since I am novice in this, I would like to get some help.#     lhs    rhs               support     confidence  lift#  1  {}  => {hi=19, 96, 108}  0.001021696 0.001021696 1   #  2  {}  => {hi=176}          0.001021696 0.001021696 1   #  3  {}  => {hi=88, 108}      0.001021696 0.001021696 1   #  4  {}  => {hi=72}           0.001051746 0.001051746 1   #  5  {}  => {hi=88, 96}       0.001051746 0.001051746 1   #  6  {}  => {hi=108, 112}     0.001081796 0.001081796 1   #  7  {}  => {hi=84}           0.001081796 0.001081796 1   #  8  {}  => {hi=100, 103}     0.001111846 0.001111846 1   #  9  {}  => {hi=18, 108, 111} 0.001111846 0.001111846 1  
Association rule mining interpretation
machine learning;r;association rules