### Test Information

Description This exam covers chapters 1-5. You must complete this exam before Sunday, June 30, 2019 at 11:59PM EASTERN Time Not allowed. This test can only be taken once. This test can be saved and resumed later.

### QUESTION 1

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 Reduction b. The Curse of Dimensionality c. The Phenomenon of Dimensionality d. The Curse of Reduction

4 points

### QUESTION 2

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

4 points

### QUESTION 3

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

4 points

### QUESTION 4

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

4 points

### QUESTION 5

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

4 points

### QUESTION 6

1. What rule (i.e. R1, R2, R3, R4, or R5) would you use for the hawk and for the grizzly bear?
 a. R2 and R5 b. R1 and R3 c. None of the above d. R1 and R4

4 points

### QUESTION 7

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

4 points

### QUESTION 8

1. This image is an example of…
 a. Genomic sequence data b. Noise c. Sequences of transactions d. Spatio-Temporal Data

4 points

### QUESTION 9

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

4 points

### QUESTION 10

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

4 points

### QUESTION 11

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

4 points

### QUESTION 12

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 13

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. stop / repeat step 2 and 3 c. stop / repeat steps 1-4 d. repeat steps 1-4 / stop

4 points

### QUESTION 14

1. Which type of Sampling is this?As each item is selected, it is removed from the population
 a. Simple Random Sampling b. Stratified sampling c. Sampling without replacement d. Sampling with replacement

4 points

### QUESTION 15

1. What is the name of the node represented by “Age”?
 a. Root node b. Branch c. Internal node d. Leaf node

4 points

### QUESTION 16

1. These images are examples of __________________.

4 points

### QUESTION 17

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

4 points

### QUESTION 18

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 19

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

4 points

### QUESTION 20

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

4 points

### QUESTION 21

1. Solve this equation for P(M/S)

4 points

### QUESTION 22

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

4 points

### QUESTION 23

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

4 points

### QUESTION 24

1. Support Vector MachinesWhich is hyperplane is better between B1 and B2?
 a. Neither B1 nor B2 b. Both B1 and B2 are the same c. B1 is better than B2 d. B2 is better than B1

4 points

### QUESTION 25

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

4 points

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