Question 1

A particular categorical attribute value has a predictiveness score of 0.5 and a predictability score of 1.0. The attribute value is

Accepted Answer

A)  necessary but not sufficient for class membership. 
B)  sufficient but not necessary for class membership. 
C)  necessary and sufficient for class membership. 
D)  neither necessary nor sufficient for class membership. 
A)  necessary but not sufficient for class membership. 
B)  sufficient but not necessary for class membership. 
C)  necessary and sufficient for class membership. 
D)  neither necessary nor sufficient for class membership. A

Question 2

Which relationship is likely to be seen with an interesting clustering of data instances?

Accepted Answer

A)  The domain resemblance score is greater than the resemblance scores for the individual clusters. 
B)  The domain resemblance score is equal to the average of the resemblance scores for the individual clusters. 
C)  The resemblance scores for all formed clusters are greater than zero. 
D)  The domain resemblance score is less than the resemblance scores for the individual clusters. 
A)  The domain resemblance score is greater than the resemblance scores for the individual clusters. 
B)  The domain resemblance score is equal to the average of the resemblance scores for the individual clusters. 
C)  The resemblance scores for all formed clusters are greater than zero. 
D)  The domain resemblance score is less than the resemblance scores for the individual clusters. D

Question 3

A particular categorical attribute value has a predictiveness score of 1.0 and a predictability score of 0.50. The attribute value is

Accepted Answer

A)  necessary but not sufficient for class membership. 
B)  sufficient but not necessary for class membership. 
C)  necessary and sufficient for class membership. 
D)  neither necessary nor sufficient for class membership. 
A)  necessary but not sufficient for class membership. 
B)  sufficient but not necessary for class membership. 
C)  necessary and sufficient for class membership. 
D)  neither necessary nor sufficient for class membership. B

Question 4

This iDA component allows us to decide if we wish to process an entire dataset or to extract a representative subset of the data for mining.

Accepted Answer

A)  preprocessor 
B)  heuristic agent 
C)  ESX 
D)  RuleMaker 
A)  preprocessor 
B)  heuristic agent 
C)  ESX 
D)  RuleMaker

Question 5

The first row of an iDAV formatted file contains attribute names. The second row reflects attribute types. What is specified in the third row of an iDAV formatted file?

Accepted Answer

A)  attribute predictability 
B)  attribute tolerance 
C)  attribute similarity 
D)  attribute usage 
A)  attribute predictability 
B)  attribute tolerance 
C)  attribute similarity 
D)  attribute usage

Question 6

The single best representative of a class.

Accepted Answer

A)  mean 
B)  centroid 
C)  signature 
D)  prototype 
A)  mean 
B)  centroid 
C)  signature 
D)  prototype

Question 7

ESX represents the overall similarity of the exemplars contained in an individual class by computing a ____ score.

Accepted Answer

A)  class resemblance 
B)  class predictability 
C)  class predictiveness 
D)  typicality 
A)  class resemblance 
B)  class predictability 
C)  class predictiveness 
D)  typicality

Question 8

A dataset of 1000 instances contains one attribute specifying the color of an object. Suppose that 800 of the instances contain the value red for the color attribute. The remaining 200 instances hold green as the value of the color attribute. What is the domain predictability score for color = green?

Accepted Answer

A)  0.80 
B)  0.20 
C)  0.60 
D)  0.40 
A)  0.80 
B)  0.20 
C)  0.60 
D)  0.40

Question 9

A particular categorical attribute value has a predictiveness score of 0.3 and a predictability score of 0.3. The attribute value is

Accepted Answer

A)  necessary but not sufficient for class membership. 
B)  sufficient but not necessary for class membership. 
C)  necessary and sufficient for class membership. 
D)  neither necessary nor sufficient for class membership. 
A)  necessary but not sufficient for class membership. 
B)  sufficient but not necessary for class membership. 
C)  necessary and sufficient for class membership. 
D)  neither necessary nor sufficient for class membership.

Question 10

Concept class $C _ { 1 }$ shows the following information for the categorical attribute Risk Factor. Use this information to answer questions 9 and 10.

$\begin{array} { l l c }\underline{ \text { Attribute Name }} &\underline{ \text { Value }} &\underline{ \text { Frequency }} \ \text { Risk factor } & \text { High Risk } & 25 \ & \text { Medium Risk } & 10 \ & \text { Low Risk } & 5 \end{array}$
-Suppose that the predictiveness score for risk factor = medium risk is 0.50. How many domain instances have a value of medium risk for the risk factor attribute?

Accepted Answer

A)  10 
B)  20 
C)  30 
D)  40 
A)  10 
B)  20 
C)  30 
D)  40

Question 11

A certain dataset contains two classesF1F1F1S1F1F1F10 class A and class BF1F1F1S1F1F1F10 each having 100 instances. RuleMaker generates several rules for each class. 
One rule for class A is given as att1 = value1
# covered = 20
# remaining =60
What percent of the class A instances are covered by this rule?

Accepted Answer

A)  20 
B)  40 
C)  60 
D)  70 
E)  80 
A)  20 
B)  40 
C)  60 
D)  70 
E)  80

Question 12

Concept class $C _ { 1 }$ shows the following information for the categorical attribute Risk Factor. Use this information to answer questions 9 and 10.

$\begin{array} { l l c }\underline{ \text { Attribute Name }} &\underline{ \text { Value }} &\underline{ \text { Frequency }} \ \text { Risk factor } & \text { High Risk } & 25 \ & \text { Medium Risk } & 10 \ & \text { Low Risk } & 5 \end{array}$
-What is the predictability score for the attribute value medium risk?

Accepted Answer

A)  0.10 
B)  0.20 
C)  0.25 
D)  0.50 
A)  0.10 
B)  0.20 
C)  0.25 
D)  0.50

A particular categorical attribute value has a predictiveness score of 0.5 and a predictability score of 1.0. The attribute value is

Which relationship is likely to be seen with an interesting clustering of data instances?

A particular categorical attribute value has a predictiveness score of 1.0 and a predictability score of 0.50. The attribute value is

This iDA component allows us to decide if we wish to process an entire dataset or to extract a representative subset of the data for mining.

The first row of an iDAV formatted file contains attribute names. The second row reflects attribute types. What is specified in the third row of an iDAV formatted file?

The single best representative of a class.

ESX represents the overall similarity of the exemplars contained in an individual class by computing a ____ score.

A particular categorical attribute value has a predictiveness score of 0.3 and a predictability score of 0.3. The attribute value is

A certain dataset contains two classes class A and class B each having 100 instances. RuleMaker generates several rules for each class. One rule for class A is given as att1 = value1 # covered = 20 # remaining =60 What percent of the class A instances are covered by this rule?

Concept class $C _ { 1 }$ shows the following information for the categorical attribute Risk Factor. Use this information to answer questions 9 and 10. Risk factor High Risk 25 Medium Risk 10 Low Risk 5 -What is the predictability score for the attribute value medium risk?

Data Mining: a First View

Data Mining: a Closer Look

Basic Data Mining Techniques

Knowledge Discovery in Databases

The Data Warehouse

Formal Evaluation Techniques

Neural Networks

Building Neural Networks With Ida

Statistical Techniques

Specialized Techniques

Rule-Based Systems

Managing Uncertainty in Rule-Based Systems

Intelligent Agents

Filters

Exam 4: An Excel-Based Data Mining Tool

A particular categorical attribute value has a predictiveness score of 0.5 and a predictability score of 1.0. The attribute value is

Which relationship is likely to be seen with an interesting clustering of data instances?

A particular categorical attribute value has a predictiveness score of 1.0 and a predictability score of 0.50. The attribute value is

This iDA component allows us to decide if we wish to process an entire dataset or to extract a representative subset of the data for mining.

The first row of an iDAV formatted file contains attribute names. The second row reflects attribute types. What is specified in the third row of an iDAV formatted file?

The single best representative of a class.

ESX represents the overall similarity of the exemplars contained in an individual class by computing a ____ score.

A particular categorical attribute value has a predictiveness score of 0.3 and a predictability score of 0.3. The attribute value is

A certain dataset contains two classes class A and class B each having 100 instances. RuleMaker generates several rules for each class. One rule for class A is given as att1 = value1 # covered = 20 # remaining =60 What percent of the class A instances are covered by this rule?

Concept class C1C _ { 1 }C1​ shows the following information for the categorical attribute Risk Factor. Use this information to answer questions 9 and 10. Risk factor High Risk 25 Medium Risk 10 Low Risk 5 -What is the predictability score for the attribute value medium risk?

Data Mining: a First View

Data Mining: a Closer Look

Basic Data Mining Techniques

Knowledge Discovery in Databases

The Data Warehouse

Formal Evaluation Techniques

Neural Networks

Building Neural Networks With Ida

Statistical Techniques

Specialized Techniques

Rule-Based Systems

Managing Uncertainty in Rule-Based Systems

Intelligent Agents

Filters

Concept class $C _ { 1 }$ shows the following information for the categorical attribute Risk Factor. Use this information to answer questions 9 and 10. Risk factor High Risk 25 Medium Risk 10 Low Risk 5 -What is the predictability score for the attribute value medium risk?