Deck 3: Basic Data Mining Techniques

Question 1

Association rule support is defined as

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.

Answer 1

C

Question 2

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

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

Answer 2

B

Question 3

Use these tables to answer questions 5 and 6.

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

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

Answer 3

D

Question 4

Which statement is true about the K-Means algorithm?

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.

Question 5

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

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.

Question 6

An evolutionary approach to data mining.

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

Question 7

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

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 8

The K-Means algorithm terminates when

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.

Question 9

Use the confusion matrix for Model X and confusion matrix for Model Y to answer questions 4 through 6.

A data mining algorithm is unstable if

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 10

Use these tables to answer questions 5 and 6.

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:

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.

Question 12

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

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

Question 13

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

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