Exam 4: Data Mining for Business Intelligence

Data mining is a way for companies to develop business intelligence from their data to gain a better understanding of their customers and operations and to solve complex organizational problems.

List three of the major characteristics and objectives of data mining.

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

Data that has a meaningful, or nonarbitrary, zero point is ________ data.

Two types of categorical data are nominal data and ordinal data.

A common example of interval scale measurement is temperature on the Celsius scale.

________ tells the nature of future occurrences of certain events based on what has happened in the past, such as predicting the winner of the Super Bowl or forecasting the absolute temperature of a particular day.

Predictions tell the nature of future occurrences of certain events based on what has happened in the past, such as predicting the winner of the Super Bowl or forecasting the absolute temperature of a particular day.

Why has data mining gained the attention of the business world?

________, or supervised induction, is perhaps the most common of all data mining tasks. Its objective is to analyze the historical data stored in a database and automatically generate a model that can predict future behavior.

List the four data preprocessing steps.

Cross-Industry Standard Process for Data Mining, or CRISP-DM, is one of the most popular nonproprietary standard methodologies for data mining.

Data types such as date/time, unstructured text, image, and audio need to be converted into some form of categorical or numeric representation before they can be processed by data mining algorithms.

In order to be applied successfully, a data mining study must be viewed as a set of automated software tools and techniques.

The model's ability to make reasonably accurate predictions, given noisy data or data with missing and erroneous values, is called ________.

________ data mining begins with a proposition by the user, who then seeks to validate the truthfulness of the proposition

The most commonly used measure to calculate the closeness between pairs of items in cluster analysis is the ________.

________ data mining begins with a proposition by the user, who then seeks to validate the truthfulness of the proposition. For example, a marketing manager may begin with the following proposition: "Are BluRay player sales related to sales of HDTV sets?"

________ measures the extent of uncertainty or randomness in a dataset.

Using existing and relevant data, data mining builds models to identify ________ among the attributes presented in the dataset.