Mining association rules in large databases Questions

1. Define the terms: frequent itemsets, patterns, and market basket analysis.
Illustrate market basket analysis
2. What is meant by association rule mining? Explain the process of association rule
mining, using an example.
3. Explain the criteria used for classifying the forms of frequent pattern mining 21 to 27
4. Explain the steps in Apriori algorithm used for frequent pattern mining with the
help of an example
5. Write the Apriori algorithm for frequent pattern mining 43 to 48
6. Explain the procedure for generating strong association rules from frequent
itemsets
7. Explain the different algorithms for improving the efficiency of the Apriori
algorithm
8. Explain a method for finding frequent patterns without generating frequent
itemsets
9. Write and explain the FP‐growth algorithm 72 to 75
10. Explain the method of using vertical data format for generating frequent
itemsets
11. What is a closed frequent itemset? Explain the approaches to mining closed
frequent itemsets.
12. Explain about mining multilevel association rules using top‐down approach, with
a suitable example
13. Explain about the variations to the top‐down approach in multilevel association
rule mining
14. Explain about multidimensional association rules using suitable examples 107 to 112
15. What is a categorical attribute? What is a quantitative attribute? 112 and 113
16. Explain the approaches for categorizing the techniques for the mining of
quantitative attributes for multidimensional association rules
17. Explain about mining multidimensional association rules using static
discretization of quantitative attributes
18. Write about mining quantitative association rules 122 to 131
19. Explain about mining for distance‐based association rules 132 to 141
20. Strong association rules are not necessarily interesting. Justify whether you
agree with this or not
21. Compare correlation analysis with association analysis in association rule mining 147
22. Explain the random walk algorithm 154
23. Explain the features of constraint‐based mining 155 to 157
24. Illustrate metarule guided mining for association rules 158 to 163
25. Explain about mining guided by additional rule constraints 164 to 172