Databases Question
College of Computing and Informatics Assignment 2 Deadline: Day 12/05/2024 @ 23:59 [Total Mark for this Assignment is 8] Student Details: Name: ID: CRN: Instructions: • You must submit two separate copies (one Word file and one PDF file) using the Assignment Template on Blackboard via the allocated folder. These files must not be in compressed format. • It is your responsibility to check and make sure that you have uploaded both the correct files. • Zero mark will be given if you try to bypass the SafeAssign (e.g. misspell words, remove spaces between words, hide characters, use different character sets, convert text into image or languages other than English or any kind of manipulation). • Email submission will not be accepted. • You are advised to make your work clear and well-presented. This includes filling your information on the cover page. • You must use this template, failing which will result in zero mark. • You MUST show all your work, and text must not be converted into an image, unless specified otherwise by the question. • Late submission will result in ZERO mark. • The work should be your own, copying from students or other resources will result in ZERO mark. Restricted -•مقيدUse Times New Roman font for all your answers. Question One Pg. 01 Learning Outcome(s): Question One 3 Marks A database has four transactions. Let min sup = 60% and min conf = 80%. Instructors: State the Learning Outcome(s) that match this question CusID TID items bought (in the form of brand-item category) 01 T100 {King’s-Crab, Sunset-Milk, Dairyland-Cheese, Best-Bread} 02 T200 {Best-Cheese, Dairyland-Milk, Goldenfarm-Apple, Tasty-Pie, WonderBread} 01 T300 {Westcoast-Apple, Dairyland-Milk, Wonder-Bread, Tasty-Pie} 03 T400 {Wonder-Bread, Sunset-Milk, Dairyland-Cheese} (a) At the granularity of item category (e.g., itemi could be “Milk”), for the rule template, ∀X ∈ transaction, buys(X,item1) ∧ buys(X,item2) ⇒ buys(X,item3) [s,c], list the frequent k-itemset for the largest k, and all the strong association rules (with their support s and confidence c) containing the frequent k-itemset for the largest k. (b) At the granularity of brand-item category (e.g., itemi could be “Sunset-Milk”), for the rule template, ∀X ∈ customer, buys(X,item1) ∧ buys(X,item2) ⇒ buys(X,item3), list the frequent k-itemset for the largest k . Restricted – مقيد Question Two Pg. 02 Learning Outcome(s): Question Two 2 Marks Suppose as manager of a chain of stores, you would like to use sales transactional data to Instructors: State the Learning Outcome(s) that match this question analyze the effectiveness of your store’s advertisements. In particular, you would like to study how specific factors influence the effectiveness of advertisements that announce a particular category of items on sale. The factors to study are the region in which customers live and the day-of-the-week and time-of-the-day of the ads. Discuss how to design an efficient method to mine the transaction data sets and explain how multidimensional and multilevel mining methods can help you derive a good solution. Restricted – مقيد Question Three Pg. 03 Learning Outcome(s): Instructors: State the Learning Outcome(s) that match this question Restricted – مقيد Question Three 3 Marks Both k-means and k-medoids algorithms can perform effective clustering. (a) Illustrate the strength and weakness of k-means in comparison with k-medoids. (b) Illustrate the strength and weakness of these schemes in comparison with a hierarchical clustering scheme (e.g., AGNES)
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