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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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Raccolta temi 2007 2010
University study material for Data Mining and Text Mining in the Computer Engineering degree programme at Politecnico di Milano. The document covers: NAME MATRICOLA Politecnico di Milano Facoltà di Ingegneria dell’Informazione Machine Learning and Data Mining Tecniche di Apprendimento Automatico per Applicazioni di Data Mining Prof. Pier Luca Lanzi 29 Giugno 2007 Solve the following problems and write the answer inside the
Data Mining and Text MiningOther
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University study material for Data Mining and Text Mining in the Computer Engineering degree programme at Politecnico di Milano. The document covers: NAME MATRICOLA Politecnico di Milano Facoltà di Ingegneria dell’Informazione Machine Learning and Data Mining Tecniche di Apprendimento Automatico per Applicazioni di Data Mining Prof. Pier Luca Lanzi 29 Giugno 2007 Solve the following problems and write the answer inside the
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NAME MATRICOLA Politecnico di Milano Facoltà di Ingegneria dell’Informazione Machine Learning and Data Mining Tecniche di Apprendimento Automatico per Applicazioni di Data Mining Prof. Pier Luca Lanzi 29 Giugno 2007 Solve the following problems and write the answer inside the problem box. The final consists of 5 sheets of paper. It must be returned with all the 5 sheets. No any other sheet can be Grades Machine Learning and Data Mining Problems 1, 2, 5, 6, and 7 Tecniche di Apprendimento Automatico per Applicazioni di Data Mining Problems 1, 2, 3, 4, and 7 Students who completed the term project don’t have to answer to problem 7. Problem 1. Consider the following training set in the 2-dimensional Euclidean space. X and Y are the attributes, “Class” is the target class. (Suggestion: plot the data). X Y Class -1 1 - 0 1 + 0 2 - 1 -1 - 1 0 + 1 2 + 2 2 - 2 3 + (a) What is the class predicted by the 3-nearest-neighbor classifier for the example (1,1)? (b) What is the class predicted by the 5-nearest-neighbor classifier for the example (1,1)? (c) What is the class predicted by the 7-nearest-neighbor classifier for the example (1,1)? Problem 2. Suppose you are given the following set of data with three Boolean input variables a; b; and c, and a single Boolean output variable K. Assume we are using a naive Bayes classifier to predict the value of K from the values of the other variables. According to the naive Bayes classifier, what is the probability of class K=1 when a=1, b=1, and c=0? According to the naive Bayes classifier, what is the probability of class K=1 when a=1, b=1, and c is unknown? Problem 3. What are association rules? What is the goal of association rule mining? Problem 4. What is a decision tree? Is it true or false that decision tree mining can be applied to…
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