HOMEWORK

1 hour, 28 minutes, 34 seconds.

Question 1

What rule (i.e. R1, R2, R3, R4, or R5) would you use for the hawk and for the grizzly bear?

a.None of the above
b.R1 and R4
c.R1 and R3
d.R2 and R5

4 points  

Question 2

What is the values of Cheat in the test data column?

4 points  

Question 3

  1. Visualization is the conversion of _________ into _________ or tabular format.
a.visual / data
b.continuous data / distrubuted
c.data / visual
d.data input / raw data

4 points  

Question 4

  1. In data mining, “Closing the loop” is a phrase most often used for: 
a.extremely labor intensive processes.
b.stopping the emergence of more complex data objects.
c.adopting ideas from other areas.
d.referring to the process of integrating data mining results into decision support systems.

4 points  

Question 5

  1. Which one of the following is NOT a challenge that motivated the development of data mining.
a.Measurement Errors
b.High Dimensionality
c.Non-traditional Analysis
d.Data Ownership and Distribution

4 points  

Question 6

  1. It is not possible for some of the child nodes to be empty (i.e. there are no records associated with these nodes).

True

False

4 points  

Question 7

  1. Data mining is a technology that blends traditional data analysis methods with sophisticated algorithms for processing large volumes of data.

True

False

4 points  

Question 8

  1. Four of the core data mining tasks are: Anomaly Detection, Association Analysis, Predictive Modeling, and Cluster Analysis

True

False

4 points  

Question 9

Solve this equation for P(M/S)

4 points  

Question 10

These images are examples of __________________.

4 points  

Question 11

  1. Data cubes may have either more or fewer than ______ dimension(s).
a.Two
b.One
c.Three
d.Four

4 points  

Question 12

  1. Which type of Sampling is this?

As each item is selected, it is removed from the population

a.Simple Random Sampling
b.Sampling with replacement
c.Sampling without replacement
d.Stratified sampling

4 points  

Question 13

  1. Support Vector Machines

Which is hyperplane is better between B1 and B2?

a.Both B1 and B2 are the same
b.B2 is better than B1
c.Neither B1 nor B2
d.B1 is better than B2

4 points  

Question 14

  1. What is the phenomenon that many types of data analysis become significantly harder as the dimensionality of the data increases?
a.The Phenomenon of Dimensionality
b.The Phenomenon of  Reduction
c.The Curse of Dimensionality
d.The Curse of Reduction

4 points  

Question 15

  1. What does ACCENT principles stand for:
a.Analysis, capacity, classification, efficiency, nearest-neighbor, and transformation
b.Association, clarity, classification, efficiency, necessity, and training, 
c.Apprehension, clarity, consistency, efficiency, necessity, and truthfulness
d.Attribute, classification, clarity, estimation, node, and transaction

4 points  

Question 16

What is the name of the node represented by “Age”?

a.Branch
b.Root node
c.Internal node
d.Leaf node

4 points  

Question 17

  1. Data exploration can aid in the selecting of the appropriate post-processing and data analysis techniques.

True

False

4 points  

Question 18

  1. Data mining is an integral part of knowledge discovery in database (KDD), which is the overall process of converting ____ into _____.
a.input data / data fusion
b.primary data / secondary data
c.input data / output data
d.raw data / useful information

4 points  

Question 19

  1. Steps in Decision Trees
    1. Choose Best attribute
    2. Extend tree by adding branch for each attribute values
    3. Sort training examples to leaf nodes
    4. if all/most training examples are being classified, then ______ else ________ for leaf nodes.
a.repeat step 1 / repeat step 2
b.repeat steps 1-4 / stop
c.stop / repeat steps 1-4
d.stop / repeat step 2 and 3

4 points  

Question 20

  1. In this technique, each attribute is associated with a specific feature of a face, and the attribute value is used to determine the way a facial feature is expressed.  This technique is called._______________

4 points  

Question 21

  1. The Bayes theorem is a statistical principle for combining prior knowledge of the classes with new evidence gathered from data.

True

False

4 points  

Question 22

  1. Name the four (4) types of attributes:
a.Distinctiveness, Order, Addition, and Multiplication
b.Nominal, Ordinal, Interval, and Ratio
c.Nominal, Ordinal,  Addition, and Multiplication
d.Distinctiveness, Order, Interval, and Ratio

4 points  

Question 23

  1. Match the following Feature Subset Selection
Embedded aproach 
 
Brute-force approach
Filter approach
  1.  
  2.  
Wrapper approacha. Features are selected before data mining algorithm is run. b. Use the data mining algorithm as a black box to find best subset of attributes. c. Feature selection occurs naturally as part of the data mining algorithm. d. Try all possible feature subsets as input to data mining algorithm.

4 points  

Question 24

  1. Which of the following is NOT a measure of node impurity:
a.Entropy
b.Classification error
c.Gini Index
d.Gain Ratio

4 points  

Question 25

This image is an example of…

a.Noise
b.Spatio-Temporal Data
c.Sequences of transactions
d.Genomic sequence data

4 points  

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