PyQdeck LogoPyQdeckSubjects
Back to the 2022 paper

Module 2: Data Mining and Association Rule Mining

20222m

The computational complexity of Apriori algorithm increases with the _______ in the bound of support threshold.

(i) increase
(ii) decrease
(iii) Does not depend
(iv) None of the above

Search GoogleAsk AI
Worked SolutionAI Assisted

Answer: (ii) decrease

Apriori's runtime is dominated by candidate generation and database scans. As min_sup decreases, more itemsets clear the support bar and become "frequent" — so:

  • More frequent itemsets survive at every level (L1, L2, L3, ...).
  • More candidate itemsets get generated at each join step.
  • More database scans/counting passes are needed to evaluate them.
min_sup HIGH  → few frequent itemsets → fast (small search space)
min_sup LOW   → many frequent itemsets → slow (huge search space)

So computational complexity increases as the support threshold decreases — a lower bar lets far more candidates through, exploding the search space combinatorially.

Similar questions

Data MiningA database has five transactions. Let min sup = 60% and min con f = 80% : | 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} | Find all frequent item sets using Apriori and FP-growth, respectively. Compare the efficiency of the two mining processes.202214mData MiningA database has five transactions. Let min_sup = 60% and min_conf = 80%. | 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} | (i) Find all frequent item sets using a priori algorithm. (ii) List all the strong association rules (with support s and confidence c) matching the following metarule, where X is a variable representing customers: \forall x \in \text{transactions}, \text{buys}(X, \text{item}_1) \wedge \text{buys}(X, \text{item}_2) \Rightarrow \text{buys}(X, \text{item}_3)\ [s, c]20218mData MiningWrite and explain pseudocode for a priori algorithm. Explain the terms: (i) support count; (ii) confidence.20206mDesign & Analysis of AlgorithmsDiscuss the average, worst, and best time complexity of the algorithm. Give suitable examples.20237m
PreviousThe total number of possible rule extracted from a data set that contains d item is (i) R = 3^{d+1} - 2^d + 1 (ii) R = 3^{d+1} - 2^{d+1} + 1 (iii) R = 3^d - 2^{d+1} + 1 (iv) R = 3^d - 2^d + 1NextThe training time is _______ and testing time is _______ of ANN. (i) low, high (ii) high, low (iii) low, low (iv) high, high