Exam 4: Data Mining

If using a mining analogy,"knowledge mining" would be a more appropriate term than "data mining."

What are the differences between nominal,ordinal,interval and ratio data? Give examples.

In the Target case study,why did Target send a teen maternity ads?

Data are often buried deep within very large ________,which sometimes contain data from several years.

The data mining in cancer research case study explains that data mining methods are capable of extracting patterns and ________ hidden deep in large and complex medical databases.

The data field "ethnic group" can be best described as

List six common data mining mistakes.

In ________,a classification method,the complete data set is randomly split into mutually exclusive subsets of approximately equal size and tested multiple times on each left-out subset,using the others as a training set.

Which of the following is a data mining myth?

Customer ________ management extends traditional marketing by creating one-on-one relationships with customers.

What does the scalability of a data mining method refer to?

Statistics and data mining both look for data sets that are as large as possible.

Knowledge extraction,pattern analysis,data archaeology,information harvesting,pattern searching,and data dredging are all alternative names for ________.

Data preparation,the third step in the CRISP-DM data mining process,is more commonly known as ________.

In the Cabela's case study,what types of models helped the company understand the value of customers,using a five-point scale?

While prediction is largely experience and opinion based,________ is data and model based.

In the Memphis Police Department case study,predictive analytics helped to identify the best schedule for officers in order to pay the least overtime.

Prediction problems where the variables have numeric values are most accurately defined as

During classification in data mining,a false positive is an occurrence classified as true by the algorithm while being false in reality.

List four myths associated with data mining.