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Introduction to Data Mining Instructor’s Solution Manual Pang-Ning Tan Michael Steinbach Vipin Kumar Copyright c 2006 Pearson Addison-Wesley. All rights reserved. Contents 1 Introduction 1 2Data 5 3 Exploring Data 19 4 Classification: Basic Concepts, Decision Trees, and Model Evaluation […]

10 Anomaly Detection 1. Compare and contrast the different techniques for anomaly detection that were presented in Section 10.1.2. In particular, try to identify circumstances in which the definitions of anomalies used in the different techniques might be equivalent or […]

2 Data 1. In the initial example of Chapter 2, the statistician says, “Yes, fields 2 and 3 are basically the same.” Can you tell from the three lines of sample data that are shown why she says that? 2. […]

3 Exploring Data 1. Obtain one of the data sets available at the UCI Machine Learning Repository and apply as many of the different visualization techniques described in the chapter as possible. The bibliographic notes and book Web site provide […]

4 Classification: Basic Concepts, Decision Trees, and Model Evaluation 1. Draw the full decision tree for the parity function of four Boolean attributes, A,B,C,andD. Is it possible to simplify the tree? A B C D Class TTTTT TTTFF TTFTF TTFFT […]

58 Chapter 5 Classification: Alternative Techniques Battery Gauge Fuel P(B = bad) = 0.1 P(F = empty) = 0.2 Figure 5.4. Bayesian belief network for Exercise 12. P(Value=High|Engine=Good, Air Cond=Working) = 0.750 P(Value=High|Engine=Good, Air Cond=Broken) = 0.667 P(Value=High|Engine=Bad, Air Cond=Working) […]

5 Classification: Alternative Techniques 1. Consider a binary classification problem with the following set of attributes and attribute values: •Air Conditioner = {Working, Broken} •Engine = {Good, Bad} •Mileage = {High, Medium, Low} •Rust = {Yes, No} Suppose a rule-based […]

6 Association Analysis: Basic Concepts and Algorithms 1. For each of the following questions, provide an example of an association rule from the market basket domain that satisfies the following conditions. Also, describe whether such rules are subjectively interesting. (a) […]

83 null (b) How many leaf nodes are there in the candidate hash tree? How many internal nodes are there? Answer: There are 5 leaf nodes and 4 internal nodes. (c) Consider a transaction that contains the following items: {1,2,3,5,6}. […]

110 Chapter 7 Association Analysis: Advanced Concepts (a) Consider an item xin a given concept hierarchy. Let x1,x2,…,xk denote the kchildren of xin the concept hierarchy. Show that s(x)≤ k i=1 s(xi), where s(·) is the support of an item. […]

7 Association Analysis: Advanced Concepts 1. Consider the traffic accident data set shown in Table 7.1. Table 7.1. Traffic accident data set. Weather Driver’s Traffic Seat Belt Crash Condition Condition Violation Severity Good Alcohol-impaired Exceed speed limit No Major Bad […]

136 Chapter 8 Cluster Analysis: Basic Concepts and Algorithms For each of the following types of data or clusters, discuss briefly if (1) sam- pling will cause problems for this approach and (2) what those problems are. Assume that the […]

8 Cluster Analysis: Basic Concepts and Algorithms 1. 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 vec- tor quantization is used for compression and […]

9 Cluster Analysis: Additional Issues and Algorithms 1. For sparse data, discuss why considering only the presence of non-zero values might give a more accurate view of the objects than considering the actual magnitudes of values. When would such an […]