A database has five transactions. Let \(\min _{-} \sup =60 \%\) and \(\min _{-}\)conf \(=80 \%\).

a. Find all frequent itemsets using Apriori and FP-growth, respectively. Compare the efficiency of the two mining processes.
b. List all the strong association rules (with support \(s\) and confidence \(c\) ) matching the following metarule, where \(X\) is a variable representing customers, and item \(_{i}\) denotes variables representing items (e.g., " \(A\)," " \(B\),"):
\[
\forall x \in \text { transaction, } \text { buys }\left(X, \text { item }_{1}ight) \wedge \operatorname{buys}\left(X, \text { item }_{2}ight) \Rightarrow \text { buys }\left(X, \text { item }_{3}ight) \quad[s, c]
\]

Transcribed Image Text:

TID items_bought T100 {M, O, N, K, E, Y} T200 (D, O, N, K, E, Y } T300 M, A, K, E} T400 (M, U, C, K, Y} T500 (C, O, O, K, I, E}