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Data MiningSupport 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 generated20262mData 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 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.202214m
PreviousIf a dataset contains extreme outliers that strongly affect the mean, which measure is generally more robust for describing central tendency? (i) Mean (ii) Variance (iii) Median (iv) Standard deviationNextSupport 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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