A local retailer has a database that stores 10,000 transactions of last summer. After analyzing the data, a data science team has identified the following statistics:
{battery} appears in 6,000 transactions.
{sunscreen} appears in 5,000 transactions.
{sandals} appears in 4,000 transactions.
{bowls} appears in 2,000 transactions.
{battery,sunscreen} appears in 1,500 transactions.
{battery,sandals} appears in 1,000 transactions.
{battery,bowls} appears in 250 transactions.
{battery,sunscreen,sandals} appears in 600 transactions.
Provide response to the following questions:
What are the support values of the preceding itemsets?
Assuming the minimum support is 0.05, which itemsets are considered frequent?
What are the confidence values of {battery}→{sunscreen} and {battery,sunscreen}→{sandals}? Which of the two rules is more interesting?
List all the candidate rules that can be formed from the statistics. Which rules are considered interesting at the minimum confidence 0.25? Out of these interesting rules, which rule is considered the most useful (that is, least coincidental)?
Conduct library research and identify about three types of an algorithm that uncovers relationships among items and association rules. Compare the identified algorithm with the Apriori algorithm and properties. Also, include their pros and cons.
Peer Response:
Select at least two (2) other students’ threads and post substantive comments on those threads. Your comments should extend the conversation started with the thread.