Question 1

Which statement is true about the decision tree attribute selection process described in your book?

Accepted Answer

A)  A categorical attribute may appear in a tree node several times but a numeric attribute may appear at most once. 
B)  A numeric attribute may appear in several tree nodes but a categorical attribute may appear at most once. 
C)  Both numeric and categorical attributes may appear in several tree nodes. 
D)  Numeric and categorical attributes may appear in at most one tree node. 
A)  A categorical attribute may appear in a tree node several times but a numeric attribute may appear at most once. 
B)  A numeric attribute may appear in several tree nodes but a categorical attribute may appear at most once. 
C)  Both numeric and categorical attributes may appear in several tree nodes. 
D)  Numeric and categorical attributes may appear in at most one tree node. B

Question 2

The K-Means algorithm terminates when

Accepted Answer

A)  a user-defined minimum value for the summation of squared error differences between instances and their corresponding cluster center is seen. 
B)  the cluster centers for the current iteration are identical to the cluster centers for the previous iteration. 
C)  the number of instances in each cluster for the current iteration is identical to the number of instances in each cluster of the previous iteration. 
D)  the number of clusters formed for the current iteration is identical to the number of clusters formed in the previous iteration. 
A)  a user-defined minimum value for the summation of squared error differences between instances and their corresponding cluster center is seen. 
B)  the cluster centers for the current iteration are identical to the cluster centers for the previous iteration. 
C)  the number of instances in each cluster for the current iteration is identical to the number of instances in each cluster of the previous iteration. 
D)  the number of clusters formed for the current iteration is identical to the number of clusters formed in the previous iteration. B

Question 3

Which statement is true about the K-Means algorithm?

Accepted Answer

A)  All attribute values must be categorical. 
B)  The output attribute must be cateogrical. 
C)  Attribute values may be either categorical or numeric. 
D)  All attributes must be numeric. 
A)  All attribute values must be categorical. 
B)  The output attribute must be cateogrical. 
C)  Attribute values may be either categorical or numeric. 
D)  All attributes must be numeric. D

Question 4

A data mining algorithm is unstable if

Accepted Answer

A)  test set accuracy depends on the ordering of test set instances. 
B)  the algorithm builds models unable to classify outliers. 
C)  the algorithm is highly sensitive to small changes in the training data. 
D)  test set accuracy depends on the choice of input attributes. 
A)  test set accuracy depends on the ordering of test set instances. 
B)  the algorithm builds models unable to classify outliers. 
C)  the algorithm is highly sensitive to small changes in the training data. 
D)  test set accuracy depends on the choice of input attributes.

Question 5

Association rule support is defined as

Accepted Answer

A)  the percentage of instances that contain the antecendent conditional items listed in the association rule. 
B)  the percentage of instances that contain the consequent conditions listed in the association rule. 
C)  the percentage of instances that contain all items listed in the association rule. 
D)  the percentage of instances in the database that contain at least one of the antecendent conditional items listed in the association rule. 
A)  the percentage of instances that contain the antecendent conditional items listed in the association rule. 
B)  the percentage of instances that contain the consequent conditions listed in the association rule. 
C)  the percentage of instances that contain all items listed in the association rule. 
D)  the percentage of instances in the database that contain at least one of the antecendent conditional items listed in the association rule.

Question 6

An evolutionary approach to data mining.

Accepted Answer

A)  backpropagation learning 
B)  genetic learning 
C)  decision tree learning 
D)  linear regression 
A)  backpropagation learning 
B)  genetic learning 
C)  decision tree learning 
D)  linear regression

Question 7

Use these tables to answer questions 5 and 6.
 $\begin{array}{c}
\begin{array}{|l|}
\hline\text { Single Item Sets }\
\hline\text { Magazine Promo = Yes } \
\hline \text { Watch Promo = No } \ 
\hline\text { Life Ins Promo = Yes } \
\hline \text { Life Ins Promo = No } \
\hline \text { Card Insurance = No } \
\hline\text { Sex = Male }\
\hline
\end{array}
\begin{array}{c|}
\hline\text { Number of Items }\
\hline 7 \
\hline 6 \
\hline 5 \
\hline 5 \
\hline 8 \
\hline 6 \
\hline
\end{array}\\
\end{array}$
 $\begin{array}{c}
\begin{array}{|l|}
\hline\text { Two Item Sets }\
\
\hline \text { Magazine Promo = Yes \& Watch Promo = No } \
\hline \text { Magazine Promo = Yes \& Life Ins Promo = Yes } \
\hline \text { Magazine Promo = Yes \& Card Insurance = No } \
\hline \text { Watch Promo = No \& Card Insurance = No }\
\hline
\end{array}
\begin{array}{c|}
\hline\text { Number of}\
\text { Items}\
\hline4\
\hline 5\
\hline 5\
\hline 5\
\hline
\end{array}
\end{array}$

-Based on the two-item set table, which of the following is not a possible two-item set rule?

Accepted Answer

A)  IF Life Ins Promo = Yes THEN Magazine Promo = Yes 
B)  IF Watch Promo = No THEN Magazine Promo = Yes 
C)  IF Card Insurance = No THEN Magazine Promo = Yes 
D)  IF Life Ins Promo = No THEN Card Insurance = No 
A)  IF Life Ins Promo = Yes THEN Magazine Promo = Yes 
B)  IF Watch Promo = No THEN Magazine Promo = Yes 
C)  IF Card Insurance = No THEN Magazine Promo = Yes 
D)  IF Life Ins Promo = No THEN Card Insurance = No

Question 8

A genetic learning operation that creates new population elements by combining parts of two or more existing elements.

Accepted Answer

A)  selection 
B)  crossover 
C)  mutation 
D)  absorption 
A)  selection 
B)  crossover 
C)  mutation 
D)  absorption

Question 9

This approach is best when we are interested in finding all possible interactions among a set of attributes.

Accepted Answer

A)  decision tree 
B)  association rules 
C)  K-Means algorithm 
D)  genetic learning Computational Questions 
A)  decision tree 
B)  association rules 
C)  K-Means algorithm 
D)  genetic learning Computational Questions

Question 10

Use these tables to answer questions 5 and 6.
 $\begin{array}{c}
\begin{array}{|l|}
\hline\text { Single Item Sets }\
\hline\text { Magazine Promo = Yes } \
\hline \text { Watch Promo = No } \ 
\hline\text { Life Ins Promo = Yes } \
\hline \text { Life Ins Promo = No } \
\hline \text { Card Insurance = No } \
\hline\text { Sex = Male }\
\hline
\end{array}
\begin{array}{c|}
\hline\text { Number of Items }\
\hline 7 \
\hline 6 \
\hline 5 \
\hline 5 \
\hline 8 \
\hline 6 \
\hline
\end{array}\\
\end{array}$
 $\begin{array}{c}
\begin{array}{|l|}
\hline\text { Two Item Sets }\
\
\hline \text { Magazine Promo = Yes \& Watch Promo = No } \
\hline \text { Magazine Promo = Yes \& Life Ins Promo = Yes } \
\hline \text { Magazine Promo = Yes \& Card Insurance = No } \
\hline \text { Watch Promo = No \& Card Insurance = No }\
\hline
\end{array}
\begin{array}{c|}
\hline\text { Number of}\
\text { Items}\
\hline4\
\hline 5\
\hline 5\
\hline 5\
\hline
\end{array}
\end{array}$

-One two-item set rule that can be generated from the tables above is: If Magazine Promo = Yes Then Life Ins promo = Yes
The confidence for this rule is:

Accepted Answer

A)  5 / 7 
B)  5 / 12 
C)  7 / 12 
D)  1 
A)  5 / 7 
B)  5 / 12 
C)  7 / 12 
D)  1

Question 11

Construct a decision tree with root node Type from the data in the table below. The first row contains attribute names. Each row after the first represents the values for one data instance. The output attribute is Class.
 $$\begin{array} { | l | l | l | l | l | } 
\hline \text { Scale } & \text { Type } & \text { Shade } & \text { Texture } & \text { Class } \
\hline \text { One } & \text { One } & \text { Light } & \text { Thin } & \text { A } \
\hline \text { Two } & \text { One } & \text { Light } & \text { Thin } & \text { A } \
\hline \text { Two } & \text { Two } & \text { Light } & \text { Thin } & \text { B } \
\hline \text { Two } & \text { Two } & \text { Dark } & \text { Thin } & \text { B } \
\hline \text { Two } & \text { One } & \text { Dark } & \text { Thin } & \text { C } \
\hline \text { One } & \text { One } & \text { Dark } & \text { Thin } & \text { C } \
\hline \text { One } & \text { Two } & \text { Light } & \text { Thin } & \text { C } \
\hline
\end{array}$$

Accepted Answer

1. Here is

Question 12

Given a rule of the form IF X THEN Y, rule confidence is defined as the conditional probability that

Accepted Answer

A)  Y is true when X is known to be true. 
B)  X is true when Y is known to be true. 
C)  Y is false when X is known to be false. 
D)  X is false when Y is known to be false. 
A)  Y is true when X is known to be true. 
B)  X is true when Y is known to be true. 
C)  Y is false when X is known to be false. 
D)  X is false when Y is known to be false.

Question 13

The computational complexity as well as the explanation offered by a genetic algorithm is largely determined by the

Accepted Answer

A)  fitness function 
B)  techniques used for crossover and mutation 
C)  training data 
D)  population of elements 
A)  fitness function 
B)  techniques used for crossover and mutation 
C)  training data 
D)  population of elements

Which statement is true about the decision tree attribute selection process described in your book?

The K-Means algorithm terminates when

Which statement is true about the K-Means algorithm?

A data mining algorithm is unstable if

Association rule support is defined as

An evolutionary approach to data mining.

A genetic learning operation that creates new population elements by combining parts of two or more existing elements.

This approach is best when we are interested in finding all possible interactions among a set of attributes.

Given a rule of the form IF X THEN Y, rule confidence is defined as the conditional probability that

The computational complexity as well as the explanation offered by a genetic algorithm is largely determined by the

Data Mining: a First View

Data Mining: a Closer Look

An Excel-Based Data Mining Tool

Knowledge Discovery in Databases

The Data Warehouse

Formal Evaluation Techniques

Neural Networks

Building Neural Networks With Ida

Statistical Techniques

Specialized Techniques

Rule-Based Systems

Managing Uncertainty in Rule-Based Systems

Intelligent Agents

Filters

Exam 3: Basic Data Mining Techniques

Which statement is true about the decision tree attribute selection process described in your book?

The K-Means algorithm terminates when

Which statement is true about the K-Means algorithm?

A data mining algorithm is unstable if

Association rule support is defined as

An evolutionary approach to data mining.

A genetic learning operation that creates new population elements by combining parts of two or more existing elements.

This approach is best when we are interested in finding all possible interactions among a set of attributes.

Given a rule of the form IF X THEN Y, rule confidence is defined as the conditional probability that

The computational complexity as well as the explanation offered by a genetic algorithm is largely determined by the

Data Mining: a First View

Data Mining: a Closer Look

An Excel-Based Data Mining Tool

Knowledge Discovery in Databases

The Data Warehouse

Formal Evaluation Techniques

Neural Networks

Building Neural Networks With Ida

Statistical Techniques

Specialized Techniques

Rule-Based Systems

Managing Uncertainty in Rule-Based Systems

Intelligent Agents

Filters