2014 09 04

Pagina 1

Politecnico di Milano Facoltà di Ingegneria dell’Informazione Data Mining and Text Mining Tecniche di Apprendimento Automatico Prof. Pier Luca Lanzi & Ing. Daniele Loiacono September 4, 2014 NAME MATRICOLA Solve the following problems and write the answer inside the problem box. Answers must be clearly written. Pencils are not allowed. The midterm consists of 3 sheets of paper. It must be returned with all the 3 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. All the answers must be adequately motivated. Problems 1 to 4 assign up to 7 points each. Problem 5 assigns 5 points. Grades The image cannot be displayed. Your computer may not have enough memory to open the image, or the image may have been corrupted. Restart your computer, and then open the file again. If the red x still appears, you may have to delete the image and then insert it again. Problem 1. Apply subtree raising to the following tree: Problem 1. (continued) Problem 2. Explain the concept of optimization and completeness in the area of data mining according to what discussed during the course. Discuss these concepts in the context of decision trees and instance-based learning. Problem 3. Explain in detail the execution and the type of result produced by the following piece of code. library(foreign) library(GMD) #reads the iris dataset iris = read.arff("iris.arff") plot_wss = rep(0,12) plot_bss = rep(0,12) for(i in 1:12) { cl <- kmeans(iris[,1:4],i) plot_wss[i] <-…