Discover Association Rules from Sequential or Transaction Data

Discovers association rules using the Apriori algorithm with proper candidate pruning. Accepts netobject (extracts sequences as transactions), data frames, lists, or binary matrices.

Support counting is vectorized via crossprod() for 2-itemsets and logical matrix indexing for k-itemsets.

Usage

association_rules(
  x,
  min_support = 0.1,
  min_confidence = 0.5,
  min_lift = 1,
  max_length = 5L
)

Arguments

x

Input data. Accepts:

netobject: Uses $data sequences - each sequence becomes a transaction of its unique states.
list: Each element is a character vector of items (one transaction).
data.frame: Wide format: each row is a transaction, character columns are item occurrences. Or a binary matrix (0/1).
matrix: Binary transaction matrix (rows = transactions, columns = items).

min_support

Numeric. Minimum support threshold. Default: 0.1.

min_confidence

Numeric. Minimum confidence threshold. Default: 0.5.

min_lift

Numeric. Minimum lift threshold. Default: 1.0.

max_length

Integer. Maximum itemset size. Default: 5.

Value

An object of class "net_association_rules" containing:

rules: Data frame with columns: antecedent (list), consequent (list), support, confidence, lift, conviction, count, n_transactions.
frequent_itemsets: List of frequent itemsets per level k.
items: Character vector of all items.
n_transactions: Integer.
n_rules: Integer.
params: List of min_support, min_confidence, min_lift, max_length.

Details

Algorithm

Uses level-wise Apriori (Agrawal & Srikant, 1994) with the full pruning step: after the join step generates k-candidates, all (k-1)-subsets are verified as frequent before support counting. This is critical for efficiency at k >= 4.

Metrics

support: P(A and B). Fraction of transactions containing both antecedent and consequent.
confidence: P(B | A). Fraction of antecedent transactions that also contain the consequent.
lift: P(A and B) / (P(A) * P(B)). Values > 1 indicate positive association; < 1 indicate negative association.
conviction: (1 - P(B)) / (1 - confidence). Measures departure from independence. Higher = stronger implication.

References

Agrawal, R. & Srikant, R. (1994). Fast algorithms for mining association rules. In Proc. 20th VLDB Conference, 487–499.

Examples

# From a list of transactions
trans <- list(
  c("plan", "discuss", "execute"),
  c("plan", "research", "analyze"),
  c("discuss", "execute", "reflect"),
  c("plan", "discuss", "execute", "reflect"),
  c("research", "analyze", "reflect")
)
rules <- association_rules(trans, min_support = 0.3, min_confidence = 0.5)
print(rules)
#> Association Rules  [24 rules | 6 items | 5 transactions]
#>   Support >= 0.30  |  Confidence >= 0.50  |  Lift >= 1.00
#> 
#>   Top rules (by lift):
#>     1. analyze -> research  (sup=0.400 conf=1.000 lift=2.50)
#>     2. research -> analyze  (sup=0.400 conf=1.000 lift=2.50)
#>     3. discuss -> execute  (sup=0.600 conf=1.000 lift=1.67)
#>     4. execute -> discuss  (sup=0.600 conf=1.000 lift=1.67)
#>     5. discuss, plan -> execute  (sup=0.400 conf=1.000 lift=1.67)
#>     6. execute, plan -> discuss  (sup=0.400 conf=1.000 lift=1.67)
#>     7. discuss, reflect -> execute  (sup=0.400 conf=1.000 lift=1.67)
#>     8. execute, reflect -> discuss  (sup=0.400 conf=1.000 lift=1.67)
#>     9. discuss -> execute, plan  (sup=0.400 conf=0.667 lift=1.67)
#>     10. execute -> discuss, plan  (sup=0.400 conf=0.667 lift=1.67)
#>     ... and 14 more rules

# From a netobject (sequences as transactions)
seqs <- data.frame(
  V1 = sample(LETTERS[1:5], 50, TRUE),
  V2 = sample(LETTERS[1:5], 50, TRUE),
  V3 = sample(LETTERS[1:5], 50, TRUE)
)
net <- build_network(seqs, method = "relative")
rules <- association_rules(net, min_support = 0.1)