Introduction briefly Students are required to submit the assignment 3 to your instructor for grading. The assignments are on the assigned materials/textbook topics associated with the course modules. read the

Introduction briefly Students are required to submit the assignment 3 to your instructor for grading. The assignments are on the assigned materials/textbook topics associated with the course modules. read the following instruction and complete it to post on schedule. Consider the data set shown in Table 5.20 (439 page). (Chapter 5) (a) Compute the support for itemsets , , and by treating each transaction ID as a market basket. (b) Use the results in part (a) to compute the confidence for the association rules and . Is confidence a symmetric measure? (c) Use the results in part (c) to compute the confidence for the association rules and . Consider the transactions shown in Table 6.15, with an item taxonomy given in Figure 6.15 (515 page). (Chapter 6) (a) are the main challenges of mining association rules with item taxonomy? (b) Consider the approach where each transaction is replaced by an extended transaction that contains all the items in as well as their respective ancestors. For example, the transaction = Chips, Cookies will be replaced by = Chips, Cookies, Snack Food, Food . Use this approach to derive all frequent itemsets (up to size 4) with support 70%. (c) Consider an alternative approach where the frequent itemsets are generated one level at a time. Initially, all the frequent itemsets involving items at the highest level of the hierarchy are generated. Next, we use the frequent itemsets discovered at the higher level of the hierarchy to generate candidate itemsets involving items at the lower levels of the hierarchy. For example, we generate the candidate itemset Chips, Diet Soda only if Snack Food, Soda is frequent. Use this approach to derive all frequent itemsets (up to size 4) with support 70%. Consider a data set consisting of 220 data vectors, where each vector has 32 components and each component is a 4-byte value. Suppose that vector quantization is used for compression and that 216 prototype vectors are used. How many bytes of storage does that data set take before and after compression and what is the compression ratio? (Chapter 7). Conclusuion · Delivery: Delivered the assignments on time, and in correct format: 25 percent · Completion: Providing a thoroughly develop the document including descriptions of all questions: 25 percent · Understanding: Demonstrating a clear understanding of purpose and writing a central idea with mostly relevant facts, details, and/or explanation: 25 percent

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