ASSOCIATE: Add config and display and R scripts

[gjwgit]

#=======================================================================
# Rattle timestamp: 2024-10-01 13:38:24.888792 x86_64-pc-linux-gnu 

# Association Rules 

# The 'arules' package provides the 'arules' function.

library(arules, quietly=TRUE)

# Generate a transactions dataset.

crs$transactions <- as(crs$dataset[crs$train, crs$categoric], "transactions")

# Generate the association rules.

crs$apriori <- apriori(crs$transactions, parameter = list(support=0.100, confidence=0.100, minlen=2))

# Summarise the resulting rule set.

generateAprioriSummary(crs$apriori)

# Time taken: 0.03 secs

#=======================================================================
# Rattle timestamp: 2024-10-01 13:40:18.248229 x86_64-pc-linux-gnu 

# Relative Frequencies Plot 

# Association rules are implemented in the 'arules' package.

library(arules, quietly=TRUE)

# Generate a transactions dataset.

crs$transactions <- as(crs$dataset[crs$train,c(8, 10:11, 22)], "transactions")

# Plot the relative frequencies.

itemFrequencyPlot(crs$transactions, support=0.1, cex=0.8)

# List rules.

inspect(sort(crs$apriori, by="support"))

# Interesting Measures.

interestMeasure(sort(crs$apriori, by="support"), c("chiSquare", "hyperLift", "hyperConfidence", "leverage", "oddsRatio", "phi"), crs$transactions)

#=======================================================================
# Rattle timestamp: 2024-10-01 13:40:32.252009 x86_64-pc-linux-gnu 

# Graph of Rules 

# Use 'arulesViz' to plot a graph representing the rules.

library(arulesViz, quietly=TRUE)

# Display a graph representing the rules.

plot(crs$apriori, method="graph")