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Data Mining: Association Rule Analysis efer to page 10 for problems on association rule mining. structions: • Analyze a given dataset for frequent itemsets using the Apriori or FP-Growth method (conceptually, without actual implementation). • Derive strong association rules based on given thresholds for support and confidence. • Discuss the practical implications of these rules in a business scenario. Link: [https://drive.google.com/file/d/1wKSrun-GlxirS31Z9qoHazb9tC440AZF/view?usp=sharing]

Data Mining: Association Rule Analysis efer to page 10 for problems on association rule mining. structions: • Analyze a given dataset for frequent itemsets using the Apriori or FP-Growth method (conceptually, without actual implementation). • Derive strong association rules based on given thresholds for support and confidence. • Discuss the practical implications of these rules in a business scenario. Link: [https://drive.google.com/file/d/1wKSrun-GlxirS31Z9qoHazb9tC440AZF/view?usp=sharing]

Computer Networking: A Top-Down Approach (7th Edition)
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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Data Mining: Association Rule Analysis efer to page 10 for problems on association rule mining. structions: • Analyze a given dataset for frequent itemsets using the Apriori or FP-Growth method (conceptually, without actual implementation). • Derive strong association rules based on given thresholds for support and confidence. • Discuss the practical implications of these rules in a business scenario. Link: [https://drive.google.com/file/d/1wKSrun-GlxirS31Z9qoHazb9tC440AZF/view?usp=sharing]
Transcribed Image Text:Data Mining: Association Rule Analysis efer to page 10 for problems on association rule mining. structions: • Analyze a given dataset for frequent itemsets using the Apriori or FP-Growth method (conceptually, without actual implementation). • Derive strong association rules based on given thresholds for support and confidence. • Discuss the practical implications of these rules in a business scenario. Link: [https://drive.google.com/file/d/1wKSrun-GlxirS31Z9qoHazb9tC440AZF/view?usp=sharing]
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