association_rules: Discover Association Rules from Sequential or Transaction...

In Nestimate: Network Estimation, Bootstrap, and Higher-Order Analysis

Discover Association Rules from Sequential or Transaction Data

Description

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.

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.

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.

References

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

See Also

build_network, predict_links

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)

# 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)

Nestimate documentation built on April 20, 2026, 5:06 p.m.