# Data mining

Learning Goal: I’m working on a data analytics exercise and need an explanation and answer to help me learn.

The is over chapters 1-5. Please use this template and answer the questions on this form. Place your name at the top of this page prior to submitting (or add a cover page to this paper),

1.For each of the datasets note if data privacy is an important issue

a.Census data collected from 1900- 1950

b.IP addresses and visit times of web users who visit your website.

c.Images from Earth orbiting satellites

d.Names and addresses of people from the telephone book

e.Names and email addresses collected from the web.

2.Classify the following attributes as binary, discrete, or continuous. Also classify them as qualitative (nominal or ordinal) or quantitative (interval or ratio). Some cases may have more than one interpretation, so briefly indicate your reasoning if you think there may be some ambiguity.

Example: Age in years. Answer: Discrete, quantitative, ratio

(a) Time in terms of AM or PM.

(b) Brightness as measured by a light meter.

(c) Brightness as measured by people’s judgments.

(d) Angles as measured in degrees between 0◦ and 360◦.

(e) Bronze, Silver, and Gold medals as awarded at the Olympics.

(f) Height above sea level.

(g) Number of patients in a hospital.

(h) ISBN numbers for books. (Look up the format on the Web.)

(i) Ability to pass light in terms of the following values: opaque, translucent, transparent.

(j) Military rank.

(k) Distance from the center of campus.

(l) Density of a substance in grams per cubic centimeter.

(m) Coat check number. (When you attend an event, you can often give your coat to someone who, in turn, gives you a number that you can use to claim your coat when you leave.)

3.Which of the following quantities is likely to show more temporal autocorrelation: daily rainfall or daily temperature? Why?

4.Distinguish between noise and outliers. Be sure to consider the following questions.

a.Is noise ever interesting or desirable? Outliers?

b.Can noise objects be outliers?

c.Are noise objects always outliers?

d.Are outliers always noise objects?

e.Can noise make a typical value into an unusual one, or vice versa?

5.Discuss the advantages and disadvantages of using sampling to reduce the number of data objects that need to be displayed. Would simple random sampling (without replacement) be a good approach to sampling? Why or why not?

6.How might you address the problem that a histogram depends on the number and location of the bins?

7.Show that the entropy of a node never increases after splitting it into smaller successor nodes.

8.Compute a two-level decision tree using the greedy approach described in this chapter. Use the classification error rate as the criterion for splitting. What is the overall error rate of the induced tree?

Note: To determine the test condition at the root note, you first need to computer the error rates for attributes X, Y, and Z.

For attribute X the corresponding counts are:

 x c1 c2 0 60 60 1 40 40

For Y the corresponding counts are:

 y c1 c2 0 40 60 1 60 40

For Z the corresponding counts are:

 Z c1 c2 0 30 70 1 70 30

9.Consider a binary classification problem with the following set of attributes and attribute values:

• Air Conditioner = {Working, Broken}
• Mileage = {High, Medium, Low}
• Rust = {Yes, No}

Suppose a rule-based classifier produces the following rule set:

Mileage = High −→ Value = Low

Mileage = Low −→ Value = High

Air Conditioner = Working, Engine = Good −→ Value = High

Air Conditioner = Working, Engine = Bad −→ Value = Low

Air Conditioner = Broken −→ Value = Low (

a) Are the rules mutually exclusive?

b) Is the rule set exhaustive?

c) Is ordering needed for this set of rules?

d) Do you need a default class for the rule set?

Consider the one-dimensional data set shown below:

 X .5 3.0 4.5 4.6 4.9 5.2 5.3 5.5 7.0 9.5 Y – – + + + – – + – –

(a)Classify the data point x = 5.0 according to its 1-, 3-, 5-, and 9-nearest neighbors (using majority vote).

(b)Repeat the previous analysis using the distance-weighted voting approach.

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