Quiz #2

Question 1

Which of these is a method employed by the k-means algorithm to mitigate the effects of the random initialization trap?
  A. Random initialization escape
  B. K-means++
  C. Centroid placement
  D. K-medoids

Answer 1

B. K-means++

Question 2

In Association rules, a collection of one or more items is known as \_\_\_\_\_\_\_\_\_\_\_\_\_\_.
  A. a set of items
  B. a ruleset
  C. a set of rules
  D. an itemset

Answer 2

D. an itemset

Question 3

K-Means clustering is useful in creating non-spherical clusters.
True
False

Answer 3

False

Question 4

The increased likelihood that a rule occurs in a dataset relative to its typical rate of occurrence is known as \_\_\_\_\_\_\_\_\_\_.
  A. Lift
  B. Count
  C. Support
  D. Confidence

Answer 4

A. Lift

Question 5

As we discussed in class, the elbow method makes use of the Within Cluster Sum of Squares (WCSS) metric to suggest the appropriate value for “k”. If we keep increasing the value for “k”, what will happen to the value for WCSS?

A. The value for WCSS will tend towards 0.
B. The value for WCSS will tend towards 1.
C. The value for WCSS will eventually become negative.
D. The value for WCSS will grow infinitely.

Answer 5

A. The value for WCSS will tend towards 0.

Question 6

TID	Items Bought
T1	bread, milk, beer
T2	bread, diaper, beer, eggs
T3	milk, diaper, beer, coke
T4	bread, milk, diaper, beer
T5	bread, milk, diaper, coke

What is the support of the itemset {beer, coke} in the dataset above?

A. 4
B. 0.4
C. 0.2
D. 1

Answer 6

C. 0.2

Question 7

The Amelia package in R is useful for dealing with \_\_\_\_\_\_\_\_\_\_\_ data.
  A. imbalanced
  B. skewed
  C. missing
  D. aggregate

Answer 7

C. missing

Question 8

Association rules imply causality in that they explain why item B is bought whenever item A is bought.
True
False

Answer 8

False

Question 9

Good clustering will produce clusters with \_\_\_\_\_ inter-class similarity and \_\_\_\_\_\_ intra-class similarity.
  A. low, low
  B. low, high
  C. high, low
  D. high, high

Answer 9

B. low, high

Question 10

The anti-monotone property of support states that the support of an itemset is \_\_\_\_\_\_\_\_\_ than that of its subsets.
  A. always more
  B. always less
  C. sometimes more
  D. sometimes less

Answer 10

B. always less

D. sometimes less

Question 11

One of the strengths of association rules is that they are easy to understand.
True
False

Answer 11

True

Question 12

K-Means clustering only works with numeric data.
True
False

Answer 12

True

Question 13

Which of these is NOT a method used in choosing the appropriate value for "k"?
  A. Elbow Method
  B. A priori knowledge
  C. Gap statistic
  D. Ankle Method

Answer 13

D. Ankle Method

Question 14

Which of these is a distance measure employed by k-means clustering?
  A. Euclidean distance
  B. Centroid distance
  C. Manhattan distance
  D. Cluster distance

Answer 14

A. Euclidean distance

Question 15

A clustering method in which every object belongs to every cluster with a membership weight that goes between 0 (if it absolutely doesn’t belong to the cluster) and 1(if it absolutely belongs to the cluster) is known as \_\_\_\_\_\_\_ clustering.
  A. overlapping
  B. partitional
  C. hierarchical
  D. fuzzy

Answer 15

D. fuzzy

Question 16

The confidence of an association rule is the \_\_\_\_\_\_\_\_\_\_ of the rule.
  A. support strength
  B. complete coverage
  C. likelihood level
  D. predictive power

Answer 16

D. predictive power

Question 17

One of the ways to reduce the computational complexity of frequent itemset generation is by the use of \_\_\_\_\_\_\_\_\_\_\_\_.
  A. the apriori algorithm
  B. the FP-Growth algorithm
  C. the association rules algorithm
  D. the post-pruning algorithm

Answer 17

A. the apriori algorithm

Question 18

Clustering results in labels against previously unlabeled data, that is why it is sometimes referred to as \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_.
  A. predictive partitioning
  B. supervised labeling
  C. predictive labeling
  D. unsupervised classification

Answer 18

D. unsupervised classification

Question 19

Association rules are great with small data sets.
True
False

Answer 19

False

Question 20

Clustering results in labels against previously unlabeled data, that is why it is sometimes referred to as unsupervised classification.
True
False

Answer 20

True