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Question 1
0.1 out of 0.1 points

ROC stands for

Selected Answer:
Correctd.
Receiver Operating Characteristics



Answers:
a.
Return Operation Curve

b.
Receiver Operating Curve

c.
Rapid Operating Curve

Correctd.
Receiver Operating Characteristics



Question 2
0.1 out of 0.1 points

In holdout method, the given data is randomly partitioned into _____ independent sets.

Selected Answer:
Correctb.
two

Answers:
a.
one

Correctb.
two

c.
many

d.
none of the above

Question 3
0.1 out of 0.1 points

If an object has deviated significantly from the data set based on certain conditions such as time or location, it is considered which type of outlier?

Selected Answer:
Correcta.
Contextual Outlier

Answers:
Correcta.
Contextual Outlier

b.
Global Outlier

c.
Collective Outlier

d.
Not an outlier

Question 4
0.1 out of 0.1 points

Which one of the following is the major category of clustering methods for high-dimensional data

Selected Answer:
Correctb.
Dimensionality reduction methods

Answers:
a.
Statistical methods

Correctb.
Dimensionality reduction methods

c.
Bagging

d.
Normalization

Question 5
0 out of 0.1 points

The minimum eccentricity of all vertices is called

Selected Answer:
Incorrectb.
Radius

Answers:
Correcta.
Eccentricity

b.
Radius

c.
Diameter

d.
Peripheral Vertex

Question 6
0.1 out of 0.1 points

____________ is called structural-context similarity.

Selected Answer:
Correctc.
SimRank

Answers:
a.
In-Neighborhood

b.
out-neighborhood

Correctc.
SimRank

d.
Distance

Question 7
0.1 out of 0.1 points

____________ is a density-based clustering Method.

Selected Answer:
Correctd.
All the above

Answers:
a.
DBSCAN

b.
OPTICS

c.
DENCLUE

Correctd.
All the above

Question 8
0.1 out of 0.1 points

A ________________ is a tree like diagram that graphically displays hierarchical clustering

Selected Answer:
Correcta.
dendrogram

Answers:
Correcta.
dendrogram

b.
decision tree

c.
histogram

d.
None of the above

Question 9
0.1 out of 0.1 points

Clustering is an approach that uses which learning technique:

Selected Answer:
Correcta.
By Observation

Answers:
Correcta.
By Observation

b.
By examples

c.
By listening

d.
All of above

Question 10
0.1 out of 0.1 points

Bayesian belief networks (BBN) differ from naïve Bayesian classifiers (BC) in that :

Selected Answer:
Correctb.
BCs assume the values of the attributes in a tuple are conditionally independent, while BBNs do not make that assumption

Answers:
a.
BBNs are always more computationally more efficient than are BCs

Correctb.
BCs assume the values of the attributes in a tuple are conditionally independent, while BBNs do not make that assumption

c.
BCs are always more accurate, even when the data tuples have attribute values that are not necessarily conditionally independent

d.
No significant differences exist

Question 11
0.1 out of 0.1 points

In ___________ each cluster is represented by one of the objects of the cluster located near the center.

Selected Answer:
Correcta.
k-medoid

Answers:
Correcta.
k-medoid

b.
k-means

c.
STIRR

d.
ROCK

Question 12
0.1 out of 0.1 points

_________________ is a two steps process model that describes and distinguishes data classes or concepts.



Selected Answer:
Correctc.
Data Classification

Answers:
a.
Data selection

b.
Data Characterization

Correctc.
Data Classification

d.
Data discrimination



Question 13
0 out of 0.1 points

K-mean is a

Selected Answer:
Incorrectd.
None of the above

Answers:
a.
Method for creating clusters based on similarities amongst members of a cluster



b.
Method for clustering that requires an initial value, k, that predetermines the number of clusters to create from a given data set

Correctc.
Both a and b

d.
None of the above

Question 14
0.1 out of 0.1 points

Less time in training but more time in predicting

Selected Answer:
Correcta.
Lazy Learning

Answers:
Correcta.
Lazy Learning

b.
Eager Learning

c.
a and b

d.
None of the above

Question 15
0.1 out of 0.1 points

Hierarchical clustering methods

Selected Answer:
Correctc.
Start either with individual objects as clusters or with the entire data set as a single cluster

Answers:
a.
Are always top-down in their approach to forming hierarchies

b.
Are impervious to the choice of split / merge points

Correctc.
Start either with individual objects as clusters or with the entire data set as a single cluster

d.
All of the above

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