Question 1

The price of a 12 ounce box of cereal decreases from $3.50 to $3.00. What fraction is used to compute the percent decrease in the price of the cereal?

Accepted Answer

A)  1/3 
B)  1/5 
C)  1/6 
D)  1/7 
A)  1/3 
B)  1/5 
C)  1/6 
D)  1/7 D

Question 2

This technique uses mean and standard deviation scores to transform real-valued attributes.

Accepted Answer

A)  decimal scaling 
B)  min-max normalization 
C)  z-score normalization 
D)  logarithmic normalization 
A)  decimal scaling 
B)  min-max normalization 
C)  z-score normalization 
D)  logarithmic normalization C

Question 3

This step of the KDD process model deals with noisy data.

Accepted Answer

A)  Creating a target dataset 
B)  data preprocessing 
C)  data transformation 
D)  data mining 
A)  Creating a target dataset 
B)  data preprocessing 
C)  data transformation 
D)  data mining B

Question 4

The relational database model is designed to

Accepted Answer

A)  promote data redundancy. 
B)  minimize data redundancy. 
C)  eliminate the need for data transformations. 
D)  eliminate the need for data preprocessing. 
A)  promote data redundancy. 
B)  minimize data redundancy. 
C)  eliminate the need for data transformations. 
D)  eliminate the need for data preprocessing.

Question 5

KDD has been described as the application of ___ to data mining.

Accepted Answer

A)  the waterfall model 
B)  object-oriented programming 
C)  the scientific method 
D)  procedural intuition 
A)  the waterfall model 
B)  object-oriented programming 
C)  the scientific method 
D)  procedural intuition

Question 6

A data normalization technique for real-valued attributes that divides each numerical value by the same power of 10.

Accepted Answer

A)  min-max normalization 
B)  z-score normalization 
C)  decimal scaling 
D)  decimal smoothing 
A)  min-max normalization 
B)  z-score normalization 
C)  decimal scaling 
D)  decimal smoothing

Question 7

Attibutes may be eliminated from the target dataset during this step of the KDD process.

Accepted Answer

A)  creating a target dataset 
B)  data preprocessing 
C)  data transformation 
D)  data mining 
A)  creating a target dataset 
B)  data preprocessing 
C)  data transformation 
D)  data mining

Question 8

A common method used by some data mining techniques to deal with missing data items during the learning process.

Accepted Answer

A)  replace missing real-valued data items with class means 
B)  discard records with missing data 
C)  replace missing attribute values with the values found within other similar instances 
D)  ignore missing attribute values 
A)  replace missing real-valued data items with class means 
B)  discard records with missing data 
C)  replace missing attribute values with the values found within other similar instances 
D)  ignore missing attribute values

Question 9

This data transformation technique works well when minimum and maximum values for a real-valued attribute are known.

Accepted Answer

A)  min-max normalization 
B)  decimal scaling 
C)  z-score normalization 
D)  logarithmic normalization 
A)  min-max normalization 
B)  decimal scaling 
C)  z-score normalization 
D)  logarithmic normalization

Question 10

The choice of a data mining tool is made at this step of the KDD process.

Accepted Answer

A)  goal identification 
B)  creating a target dataset 
C)  data preprocessing 
D)  data mining 
A)  goal identification 
B)  creating a target dataset 
C)  data preprocessing 
D)  data mining

The price of a 12 ounce box of cereal decreases from $3.50 to $3.00. What fraction is used to compute the percent decrease in the price of the cereal?

This technique uses mean and standard deviation scores to transform real-valued attributes.

This step of the KDD process model deals with noisy data.

The relational database model is designed to

KDD has been described as the application of ___ to data mining.

A data normalization technique for real-valued attributes that divides each numerical value by the same power of 10.

Attibutes may be eliminated from the target dataset during this step of the KDD process.

A common method used by some data mining techniques to deal with missing data items during the learning process.

This data transformation technique works well when minimum and maximum values for a real-valued attribute are known.

The choice of a data mining tool is made at this step of the KDD process.

Data Mining: a First View

Data Mining: a Closer Look

Basic Data Mining Techniques

An Excel-Based Data Mining Tool

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 5: Knowledge Discovery in Databases

The price of a 12 ounce box of cereal decreases from $3.50 to $3.00. What fraction is used to compute the percent decrease in the price of the cereal?

This technique uses mean and standard deviation scores to transform real-valued attributes.

This step of the KDD process model deals with noisy data.

The relational database model is designed to

KDD has been described as the application of ___ to data mining.

A data normalization technique for real-valued attributes that divides each numerical value by the same power of 10.

Attibutes may be eliminated from the target dataset during this step of the KDD process.

A common method used by some data mining techniques to deal with missing data items during the learning process.

This data transformation technique works well when minimum and maximum values for a real-valued attribute are known.

The choice of a data mining tool is made at this step of the KDD process.

Data Mining: a First View

Data Mining: a Closer Look

Basic Data Mining Techniques

An Excel-Based Data Mining Tool

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