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- Politecnico di Milano
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- Computer Engineering
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- Data Mining and Text Mining
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2016 09 05
Full exam for Data Mining and Text Mining in the Computer Engineering degree programme at Politecnico di Milano. The document covers: Politecnico di Milano School of Industrial and Information Engineering Data Mining and Text Mining Prof. Pier Luca Lanzi September 5, 2016 NAME CODICE PERSONA/ID • Answers must be clearly written inside the problem box. All the answers must be adequately motivated. • Pencils are
Data Mining and Text MiningFull exam
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Full exam for Data Mining and Text Mining in the Computer Engineering degree programme at Politecnico di Milano. The document covers: Politecnico di Milano School of Industrial and Information Engineering Data Mining and Text Mining Prof. Pier Luca Lanzi September 5, 2016 NAME CODICE PERSONA/ID • Answers must be clearly written inside the problem box. All the answers must be adequately motivated. • Pencils are
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Politecnico di Milano School of Industrial and Information Engineering Data Mining and Text Mining Prof. Pier Luca Lanzi September 5, 2016 NAME CODICE PERSONA/ID • Answers must be clearly written inside the problem box. All the answers must be adequately motivated. • Pencils are not allowed. The exam consists of 5 sheets of paper. It must be returned with all the 5 sheets. No any other sheet can be added. No sheet can be removed. • This is a closed-book/closed-notes exam. • Only non-programmable calculators are allowed. • Notes/books/mobile phones are not allowed. • If you are caught using forbidden material, the exam will immediately end and an RP grade will be recorded; then, your Data Mining exam will consist of an oral examination from then on. • Scoring o A problem left unsolved will amount to zero points. o A completely wrong solution will amount to -3 points Grades Problem 1. (7 points) Apply two full steps of the HITS algorithm to the following network. Problem 2. (7pts) Given the following eight points (with (x, y) representing location) A1(2, 10), A2(2, 5), A3(8, 4), B1(5, 8), B2(7, 5), B3(6, 4), C1(1, 2), C2(4, 9) and the distance function implemented using the Euclidean distance. Apply k-Means using A1, B1, and C1 as the center of the initial clusters, respectively, and show: (a) the three cluster centers after the first round of execution and (b) the final three clusters Note: graphical representation can be used to speed up the process. Problem 2 (continued). Problem 3 (7pts). Compute the association rules using Eclat using a support of 0.4 and a confidence of 0.75
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