Deck 5: Data Mining for Business Intelligence

Question 1

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

Answer 1

True

Question 2

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

Answer 2

True

Question 3

Mass,length,time,plane angle,energy,and electric charge are examples of physical measures whose data are represented in interval scales.

Answer 3

False

Question 4

Technically speaking,data mining is a process that uses statistical,mathematical,and artificial intelligence techniques to extract and identify useful information and subsequent knowledge from large sets of data.

Question 5

Associations are a type of pattern that discovers time-ordered events,such as predicting that an existing banking customer who already has a checking account will open a savings account followed by an investment account within a year.

Question 6

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

Question 7

Data mining is a prime candidate for better management of companies that are data-rich,but knowledge-poor.

Question 8

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.

Question 9

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.

Question 10

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

Question 11

Compared to the other steps in CRISP-DM,data preprocessing consumes the most time and effort; most believe that this step accounts for roughly 80 percent of the total time spent on a data mining project

Question 12

The data mining environment is usually a client-server architecture or a Web-based information systems architecture.

Question 13

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

Question 14

Data mining requires a separate,dedicated database.

Question 15

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

Question 16

Business analytics cannot be conducted in real time because it includes components such as metrics and reengineering tools.

Question 17

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

Question 18

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

Question 19

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

Question 20

Predictions 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.

Question 21

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

A) tailored
B) casual
C) informal
D) ad hoc

Question 22

Because the latter steps in the data mining process are built on the outcome of the former ones,one should:

A) start with an understanding of the relevant data.
B) work quickly through the early steps and work in-depth on the latter steps.
C) start by cleaning the relevant data and storing it in a single data warehouse.
D) pay extra attention to the earlier steps in order not to put the whole study on an incorrect path from the onset.

Question 23

The term data mining was originally used to ________.

A) include most forms of data analysis in order to increase sales
B) describe the analysis of huge datasets stored in data warehouses
C) describe the process through which previously unknown patterns in data were discovered
D) All of the above

Question 24

A good question to ask with respect to the patterns/relationships that association rule mining can discover is "Are all association rules interesting and useful?" In order to answer such a question,association rule mining uses two common metrics ________ and ________.

A) mean; median
B) support; confidence
C) standard deviation; confidence interval
D) regression; distance measure

Question 25

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

A) associations
B) regression
C) predictions
D) clusters

Question 26

Data mining is tightly positioned at the intersection of many disciplines.Those disciplines include all of the following except:

A) management science
B) statistics
C) information systems and databases
D) logistics

Question 27

On the commercial side,the most common use of data mining has been in ________ sectors.

A) manufacturing and heath care
B) finance, retail, and health care
C) online retail and government
D) R&D and scientific

Question 28

Because the data mining is driven by experience and experimentation,depending on the problem situation and the analyst's knowledge,the whole process can be ________ and ________,i.e.,one should expect to go back and forth through the steps quite a few times.

A) simple; iterative
B) expensive; hypothetical
C) time consuming; iterative
D) time-consuming; hypothetical

Question 29

In an article in Harvard Business Review,Thomas Davenport (2006)argued that the latest strategic weapon for companies is ________.

A) customer relationship management
B) e-commerce
C) online auctions
D) analytical decision making

Question 30

Business analytics and data mining provided 1-800-Flowers with all of the following benefits except:

A) More efficient marketing campaigns.
B) Increased mailings and response rates.
C) Better customer experience and retention.
D) Increased repeat sales.

Question 31

Numeric data represent the numeric values of specific variables,which are ________ variables that can take on an infinite number of fractional values.

A) discrete
B) continuous
C) interval
D) ratio

Question 32

________ 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?"

A) Hypothesis-driven
B) Theory-driven
C) Discovery-driven
D) Data-driven

Question 33

Predictive analysis uses sophisticated algorithms that can be designed to sift through data and identify ________.

A) patterns of behavior
B) outlier
C) average statistics
D) minimum and maximum statistics

Question 34

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

A) categorical
B) nominal
C) interval
D) ratio

Question 35

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

A) More intense competition at the global scale driven by customers' ever-changing needs and wants in an increasingly saturated marketplace.
B) Consolidation and integration of database records, which enables a single view of customers and vendors.
C) Significant reduction in the cost of hardware and software for data storage and processing.
D) All of the above

Question 36

What is a major characteristic of data mining?

A) Data mining tools are readily combined with spreadsheets and other software development tools.
B) Because of the large amounts of data and massive search efforts, it is sometimes necessary to use serial processing for data mining.
C) Data are often buried within numerous small large databases, which sometimes contain data from several years.
D) The miner needs sophisticated programming skill.

Question 37

The simple split methodology splits the data into two mutually exclusive subsets called a ________ set and a ________ set.

A) training; test
B) positive; negative
C) holdout; training
D) matrix; test

Question 38

At the highest level of abstraction,data can be classified as ________ and ________.

A) alpha; numeric
B) categorical; numerical
C) nominal; ratio
D) interval; ration

Question 39

________,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.

A) Association
B) Clustering
C) Prediction
D) Classification

Question 40

________ are essentially a hierarchy of if-then statements.They are most appropriate for categorical and interval data.

A) Neural nets
B) Clusters
C) Decision trees
D) Time-series

Question 41

With ________,a fixed number of instances from the original data is sampled (with replacement)for training and the rest of the dataset is used for testing.This process is repeated as many times as desired.

Question 42

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

Question 43

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

Question 44

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

Question 45

________ data,also known as categorical data,contains both nominal and ordinal data.

Question 46

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

Question 47

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

Question 48

________ 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.

Question 49

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

Question 50

________ means that discovered patterns in a dataset hold true on new data with a sufficient degree of certainty.

Question 51

________ data can be readily represented by some sort of probability distribution.

Question 52

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

Question 53

Generally speaking,data mining tasks can be classified into three main categories: ________,association,and clustering.

Question 54

The ________ index has been used in economics to measure the diversity of a population.The same concept can be used to determine the purity of a specific class as a result of a decision to branch along a particular attribute or variable.

Question 55

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

Question 56

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.

Question 57

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

Question 58

________ data represent the labels of multiple classes used to divide a variable into specific groups.

Question 59

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

Question 60

Mass,length,time,energy,and electric charge are examples of ________ data because their scales have a nonarbitrary zero value.

Question 61

List five of the data mining mistakes often made in practice.

Question 62

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

Question 63

List the four data preprocessing steps.

Question 64

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

Question 65

Identify and describe the two types of categorical data.Give an example of each.

Question 66

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

Question 67

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

Question 68

Identify and describe the two types of numerical data.Give an example of each.

Question 69

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