Business Intelligence, 3e (Sharda/Delen/Turban)
Chapter 4 Data Mining
1) In the Cabela’s case study, the SAS/Teradata solution enabled the direct marketer to better
identify likely customers and market to them based mostly on external data sources.
2) The cost of data storage has plummeted recently, making data mining feasible for more firms.
3) Data mining can be very useful in detecting patterns such as credit card fraud, but is of little
help in improving sales.
4) If using a mining analogy, “knowledge mining” would be a more appropriate term than “data
mining.”
5) The entire focus of the predictive analytics system in the Infinity P&C case was on detecting
and handling fraudulent claims for the company’s benefit.
6) Data mining requires specialized data analysts to ask ad hoc questions and obtain answers
quickly from the system.
7) Ratio data is a type of categorical data.
8) Interval data is a type of numerical data.
9) In the Memphis Police Department case study, predictive analytics helped to identify the best
schedule for officers in order to pay the least overtime.
10) In data mining, classification models help in prediction.
11) Statistics and data mining both look for data sets that are as large as possible.
12) Using data mining on data about imports and exports can help to detect tax avoidance and
money laundering.
13) In the cancer research case study, data mining algorithms that predict cancer survivability
with high predictive power are good replacements for medical professionals.
14) During classification in data mining, a false positive is an occurrence classified as true by the
algorithm while being false in reality.
15) When training a data mining model, the testing dataset is always larger than the training
dataset.
16) When a problem has many attributes that impact the classification of different patterns,
decision trees may be a useful approach.
17) In the 2degrees case study, the main effectiveness of the new analytics system was in
dissuading potential churners from leaving the company.
18) Market basket analysis is a useful and entertaining way to explain data mining to a
technologically less savvy audience, but it has little business significance.
19) The number of users of free/open source data mining software now exceeds that of users of
commercial software versions.
20) Data that is collected, stored, and analyzed in data mining is often private and personal.
There is no way to maintain individuals’ privacy other than being very careful about physical
data security.
21) In the Cabela’s case study, what types of models helped the company understand the value of
customers, using a five-point scale?
A) reporting and association models
B) simulation and geographical models
C) simulation and regression models
D) clustering and association models
22) Understanding customers better has helped Amazon and others become more successful. The
understanding comes primarily from
A) collecting data about customers and transactions.
B) developing a philosophy that is data analytics-centric.
C) analyzing the vast data amounts routinely collected.
D) asking the customers what they want.
23) All of the following statements about data mining are true EXCEPT
A) the process aspect means that data mining should be a one-step process to results.
B) the novel aspect means that previously unknown patterns are discovered.
C) the potentially useful aspect means that results should lead to some business benefit.
D) the valid aspect means that the discovered patterns should hold true on new data.
24) What is the main reason parallel processing is sometimes used for data mining?
A) because the hardware exists in most organizations and it is available to use
B) because the most of the algorithms used for data mining require it
C) because of the massive data amounts and search efforts involved
D) because any strategic application requires parallel processing
25) The data field “ethnic group” can be best described as
A) nominal data.
B) interval data.
C) ordinal data.
D) ratio data.
26) The data field “salary” can be best described as
A) nominal data.
B) interval data.
C) ordinal data.
D) ratio data.
27) Which broad area of data mining applications analyzes data, forming rules to distinguish
between defined classes?
A) associations
B) visualization
C) classification
D) clustering
28) Which broad area of data mining applications partitions a collection of objects into natural
groupings with similar features?
A) associations
B) visualization
C) classification
D) clustering
29) The data mining algorithm type used for classification somewhat resembling the biological
neural networks in the human brain is
A) association rule mining.
B) cluster analysis.
C) decision trees.
D) artificial neural networks.
30) Identifying and preventing incorrect claim payments and fraudulent activities falls under
which type of data mining applications?
A) insurance
B) retailing and logistics
C) customer relationship management
D) computer hardware and software
31) All of the following statements about data mining are true EXCEPT
A) understanding the business goal is critical.
B) understanding the data, e.g., the relevant variables, is critical to success.
C) building the model takes the most time and effort.
D) data is typically preprocessed and/or cleaned before use.
32) Which data mining process/methodology is thought to be the most comprehensive, according
to kdnuggets.com rankings?
A) SEMMA
B) proprietary organizational methodologies
C) KDD Process
D) CRISP-DM
33) Prediction problems where the variables have numeric values are most accurately defined as
A) classifications.
B) regressions.
C) associations.
D) computations.
34) What does the robustness of a data mining method refer to?
A) its ability to predict the outcome of a previously unknown data set accurately
B) its speed of computation and computational costs in using the mode
C) its ability to construct a prediction model efficiently given a large amount of data
D) its ability to overcome noisy data to make somewhat accurate predictions
35) What does the scalability of a data mining method refer to?
A) its ability to predict the outcome of a previously unknown data set accurately
B) its speed of computation and computational costs in using the mode
C) its ability to construct a prediction model efficiently given a large amount of data
D) its ability to overcome noisy data to make somewhat accurate predictions
36) In estimating the accuracy of data mining (or other) classification models, the true positive
rate is
A) the ratio of correctly classified positives divided by the total positive count.
B) the ratio of correctly classified negatives divided by the total negative count.
C) the ratio of correctly classified positives divided by the sum of correctly classified positives
and incorrectly classified positives.
D) the ratio of correctly classified positives divided by the sum of correctly classified positives
and incorrectly classified negatives.
37) In data mining, finding an affinity of two products to be commonly together in a shopping
cart is known as
A) association rule mining.
B) cluster analysis.
C) decision trees.
D) artificial neural networks.
38) Third party providers of publicly available datasets protect the anonymity of the individuals
in the data set primarily by
A) asking data users to use the data ethically.
B) leaving in identifiers (e.g., name), but changing other variables.
C) removing identifiers such as names and social security numbers.
D) letting individuals in the data know their data is being accessed.
39) In the Target case study, why did Target send a teen maternity ads?
A) Target’s analytic model confused her with an older woman with a similar name.
B) Target was sending ads to all women in a particular neighborhood.
C) Target’s analytic model suggested she was pregnant based on her buying habits.
D) Target was using a special promotion that targeted all teens in her geographical area.
40) Which of the following is a data mining myth?
A) Data mining is a multistep process that requires deliberate, proactive design and use.
B) Data mining requires a separate, dedicated database.
C) The current state-of-the-art is ready to go for almost any business.
D) Newer Web-based tools enable managers of all educational levels to do data mining.
41) In the opening vignette, Cabela’s uses SAS data mining tools to create ________ models to
optimize customer selection for all customer contacts.
42) There has been an increase in data mining to deal with global competition and customers’
more sophisticated ________ and wants.
43) Knowledge extraction, pattern analysis, data archaeology, information harvesting, pattern
searching, and data dredging are all alternative names for ________.
44) Data are often buried deep within very large ________, which sometimes contain data from
several years.
45) ________ represent the labels of multiple classes used to divide a variable into specific
groups, examples of which include race, sex, age group, and educational level.
46) In the Memphis Police Department case study, shortly after all precincts embraced Blue
CRUSH, ________ became one of the most potent weapons in the Memphis police department’s
crime-fighting arsenal.
47) 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.
48) While prediction is largely experience and opinion based, ________ is data and model based.
49) Whereas ________ starts with a well-defined proposition and hypothesis, data mining starts
with a loosely defined discovery statement.
50) Customer ________ management extends traditional marketing by creating one-on-one
relationships with customers.
51) In the terrorist funding case study, an observed price ________ may be related to income tax
avoidance/evasion, money laundering, or terrorist financing.
52) Data preparation, the third step in the CRISP-DM data mining process, is more commonly
known as ________.
53) 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.
54) Fayyad et al. (1996) defined ________ in databases as a process of using data mining
methods to find useful information and patterns in the data.
55) 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.
56) The basic idea behind a ________ is that it recursively divides a training set until each
division consists entirely or primarily of examples from one class.
57) As described in the 2degrees case study, a common problem in the mobile
telecommunications industry is defined by the term ________, which means customers leaving.
58) Because of its successful application to retail business problems, association rule mining is
commonly called ________.
59) The ________ is the most commonly used algorithm to discover association rules. Given a
set of itemsets, the algorithm attempts to find subsets that are common to at least a minimum
number of the itemsets.
60) One way to accomplish privacy and protection of individuals’ rights when data mining is by
________ of the customer records prior to applying data mining applications, so that the records
cannot be traced to an individual.
61) List five reasons for the growing popularity of data mining in the business world.
62) What are the differences between nominal, ordinal, interval and ratio data? Give examples.
63) List and briefly describe the six steps of the CRISP-DM data mining process.
64) Describe the role of the simple split in estimating the accuracy of classification models.
65) Briefly describe five techniques (or algorithms) that are used for classification modeling.
66) Describe cluster analysis and some of its applications.
67) In the data mining in Hollywood case study, how successful were the models in predicting
the success or failure of a Hollywood movie?
68) In lessons learned from the Target case, what legal warnings would you give another retailer
using data mining for marketing?
69) List four myths associated with data mining.
70) List six common data mining mistakes.