Exam 4: Predictive Analytics I: Data Mining Process, methods, and Algorithms

Converting continuous valued numerical variables to ranges and categories is referred to as discretization.

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

List six common data mining mistakes.

Open-source data mining tools include applications such as IBM SPSS Modeler and Dell Statistica.

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

List and briefly describe the six steps of the CRISP-DM data mining process.

List four myths associated with data mining.

Patterns have been manually ________ from data by humans for centuries,but the increasing volume of data in modern times has created a need for more automatic approaches.

Which broad area of data mining applications analyzes data,forming rules to distinguish between defined classes?

The basic idea behind a(n)________ is that it recursively divides a training set until each division consists entirely or primarily of examples from one class.

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

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

Describe cluster analysis and some of its applications.

As described in the Influence Health case study,customers are more often ________ services from a variety of healthcare service providers before selecting one.

In the opening case,police detectives used data mining to identify possible new areas of inquiry.

There has been an increase in data mining to deal with global competition and customers' more sophisticated ________ and wants.

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

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

When a problem has many attributes that impact the classification of different patterns,decision trees may be a useful approach.

In the data mining in Hollywood case study,how successful were the models in predicting the success or failure of a Hollywood movie?