Exam 4: Data Mining for Business Intelligence

The ________ algorithm is the most commonly used algorithm to discover association rules.

List the six steps in the CRISP-DM Data Mining Process.

List and briefly explain four of the factors to be considered when assessing a model to be used for classification.

A query that can be run by an end user and is not programmed in advance is considered a(n) ________ query.

Data mining offers organizations a decision-enhancing environment to exploit opportunities by transforming data into a strategic weapon.

Explain the simple split methodology for classification. Explain the advantages of the k-fold cross-validation methodology over the simple split methodology.

Newer Web-based tools enable managers at all educational levels to do data mining.

________ data mining finds patterns, associations, and other relationships hidden within datasets. It can uncover facts that an organization had not previously known or contemplated.

With ________ forecasting, the data are a series of values of the same variable that is captured and stored over time. These data are then used to develop models to extrapolate the future values of the same phenomenon.

List and briefly describe three of the major types of patterns that data mining attempts to identify.

The first step in the data mining process is to understand the relevant data from the available databases.

The term data mining was originally used to ________.

________ data contains codes assigned to objects or events as labels that also represent the rank order among them; for example, credit score.

Data mining seeks to identify patterns in the data. All of the following are major types of patterns except:

The variable marital status can be categorized using the codes (1) single, (2) married, and (3) divorced. These codes are examples of ordinal data.

What is a major characteristic of data mining?

________ data contains measurements of simple codes assigned to objects as labels, such as marital status, but those labels are not measurements.

Clustering partitions a collection of things, such as objects and events presented in a structured dataset into segments whose members share similar characteristics.

At the highest level of abstraction, all data can be divided into interval data and ratio data.

Data mining requires a separate, dedicated database.