Question 1

Usage profiles for Web-based personalization contain several

Accepted Answer

A)  pageviews 
B)  clickstreams 
C)  cookies 
D)  session files 
A)  pageviews 
B)  clickstreams 
C)  cookies 
D)  session files A

Question 2

The automation of Web site adaptation involves creating and deleting

Accepted Answer

A)  index pages 
B)  cookies 
C)  pageviews 
D)  clickstreams 
A)  index pages 
B)  cookies 
C)  pageviews 
D)  clickstreams A

Question 3

At least eighty percent of the time spent on a Web-based data mining project is devoted to this.

Accepted Answer

A)  goal idenficiation 
B)  data preparation 
C)  data mining 
D)  interpretation of results 
A)  goal idenficiation 
B)  data preparation 
C)  data mining 
D)  interpretation of results B

Question 4

These can be used to help select a best subset of training data.

Accepted Answer

A)  domain resemblance scores 
B)  class resemblance scores 
C)  instance typicality scores 
D)  standard deviation scores 
A)  domain resemblance scores 
B)  class resemblance scores 
C)  instance typicality scores 
D)  standard deviation scores

Question 5

A set of pageviews requested by a single user from a Web server.

Accepted Answer

A)  index page 
B)  common log 
C)  session 
D)  page frame 
A)  index page 
B)  common log 
C)  session 
D)  page frame

Question 6

Which of the following is a fundamental difference between bagging and boosting?

Accepted Answer

A)  Bagging is used for supervised learning. Boosting is used with unsupervised clustering. 
B)  Bagging gives varying weights to training instances. Boosting gives equal weight to all training instances. 
C)  Bagging does not take the performance of previously built models into account when building a new model. With boosting each new model is built based upon the results of previous models. 
D)  With boosting, each model has an equal weight in the classification of new instances. With bagging, individual models are given varying weights. 
A)  Bagging is used for supervised learning. Boosting is used with unsupervised clustering. 
B)  Bagging gives varying weights to training instances. Boosting gives equal weight to all training instances. 
C)  Bagging does not take the performance of previously built models into account when building a new model. With boosting each new model is built based upon the results of previous models. 
D)  With boosting, each model has an equal weight in the classification of new instances. With bagging, individual models are given varying weights.

Question 7

A data file that contains session information.

Accepted Answer

A)  cookie 
B)  pageview 
C)  page frame 
D)  common log 
A)  cookie 
B)  pageview 
C)  page frame 
D)  common log

Question 8

The training phase of a textual data mining process involves

Accepted Answer

A)  removing common words from a dictionary. 
B)  creating an attribute dictionary. 
C)  determining whether a document is about the topic under investigation. 
D)  modifying an initially created attribute dictionary. 
A)  removing common words from a dictionary. 
B)  creating an attribute dictionary. 
C)  determining whether a document is about the topic under investigation. 
D)  modifying an initially created attribute dictionary.

Question 9

A data mining algorithm designed to discover frequently accessed Web pages that occur in the same order.

Accepted Answer

A)  serial miner 
B)  association rule miner 
C)  sequence miner 
D)  decision miner 
A)  serial miner 
B)  association rule miner 
C)  sequence miner 
D)  decision miner

Question 10

Which of the following problems is best solved using time-series analysis?

Accepted Answer

A)  Predict whether someone is a likely candidate for having a stroke. 
B)  Determine if an individual should be given an unsecured loan. 
C)  Develop a profile of a star athlete. 
D)  Determine the likelihood that someone will terminate their cell phone contract. 
A)  Predict whether someone is a likely candidate for having a stroke. 
B)  Determine if an individual should be given an unsecured loan. 
C)  Develop a profile of a star athlete. 
D)  Determine the likelihood that someone will terminate their cell phone contract.

Usage profiles for Web-based personalization contain several

The automation of Web site adaptation involves creating and deleting

At least eighty percent of the time spent on a Web-based data mining project is devoted to this.

These can be used to help select a best subset of training data.

A set of pageviews requested by a single user from a Web server.

Which of the following is a fundamental difference between bagging and boosting?

A data file that contains session information.

The training phase of a textual data mining process involves

A data mining algorithm designed to discover frequently accessed Web pages that occur in the same order.

Which of the following problems is best solved using time-series analysis?

Data Mining: a First View

Data Mining: a Closer Look

Basic Data Mining Techniques

An Excel-Based Data Mining Tool

Knowledge Discovery in Databases

The Data Warehouse

Formal Evaluation Techniques

Neural Networks

Building Neural Networks With Ida

Statistical Techniques

Rule-Based Systems

Managing Uncertainty in Rule-Based Systems

Intelligent Agents

Filters

Exam 11: Specialized Techniques

Usage profiles for Web-based personalization contain several

The automation of Web site adaptation involves creating and deleting

At least eighty percent of the time spent on a Web-based data mining project is devoted to this.

These can be used to help select a best subset of training data.

A set of pageviews requested by a single user from a Web server.

Which of the following is a fundamental difference between bagging and boosting?

A data file that contains session information.

The training phase of a textual data mining process involves

A data mining algorithm designed to discover frequently accessed Web pages that occur in the same order.

Which of the following problems is best solved using time-series analysis?

Data Mining: a First View

Data Mining: a Closer Look

Basic Data Mining Techniques

An Excel-Based Data Mining Tool

Knowledge Discovery in Databases

The Data Warehouse

Formal Evaluation Techniques

Neural Networks

Building Neural Networks With Ida

Statistical Techniques

Rule-Based Systems

Managing Uncertainty in Rule-Based Systems

Intelligent Agents

Filters