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Module 2: Data Mining Fundamentals and Association Rule Mining

20262m

Support of an itemset represents

(i) Probability that the consequent occurs given the antecedent
(ii) Number of attributes in the database
(iii) Frequency with which the itemset occurs in the database
(iv) Number of rules generated

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Solution

Correct Answer: (iii) Frequency with which the itemset occurs in the database

Explanation

Support tells how often an itemset appears in the transaction database (as a count or as a fraction/percentage of all transactions). Itemsets whose support is at least the minimum support threshold are called frequent itemsets.

Why other options are incorrect:

  • (i) This describes confidence: P(consequent | antecedent).
  • (ii) Number of attributes and (iv) Number of rules - not what support means.

Key Points to Remember:

  • Support(A => B) = P(A and B)
  • Confidence(A => B) = support(A and B) / support(A)

Answer: (iii) Frequency with which the itemset occurs in the database

Similar questions

Data MiningIn a database of 200 transactions, an itemset occurs in 50 transactions. Its support is (i) 10% (ii) 25% (iii) 20% (iv) 50%20262mData MiningConsider a database D, consisting of 15 transactions. Suppose minimum support count is 2 (i.e., min_sup = 20%) and minimum confidence required is 70%. Find out the frequent item set using a priori algorithm. Explain each step with diagram: | A1 | A2 | A3 | A4 | A5 | A6 | A7 | A8 | A9 | |:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 |20208mData MiningConsider the 5 transactions given below. If minimum support is 30% and minimum confidence is 80% determine the frequent itemsets and association rules using apriori algorithm. | Transaction | Items | | :---: | --- | | T1 | Bread, Jelly, Butter | | T2 | Bread, Butter | | T3 | Bread, Milk, Butter | | T4 | Coke, Bread | | T5 | Coke, Milk |20239mData 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]20218m
PreviousIn a database of 200 transactions, an itemset occurs in 50 transactions. Its support is (i) 10% (ii) 25% (iii) 20% (iv) 50%NextA classifier correctly classifies 180 out of 200 instances. Its accuracy is (i) 80% (ii) 90% (iii) 85% (iv) 95%
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