Deck 4: Data Mining

Question 1

In the cancer research case study,data mining algorithms that predict cancer survivability with high predictive power are good replacements for medical professionals.

Answer 1

False

Question 2

The number of users of free/open source data mining software now exceeds that of users of commercial software versions.

Answer 2

True

Question 3

Interval data is a type of numerical data.

Answer 3

True

Question 4

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

Question 5

Statistics and data mining both look for data sets that are as large as possible.

Question 6

Ratio data is a type of categorical data.

Question 7

When training a data mining model,the testing dataset is always larger than the training dataset.

Question 8

Data mining requires specialized data analysts to ask ad hoc questions and obtain answers quickly from the system.

Question 9

During classification in data mining,a false positive is an occurrence classified as true by the algorithm while being false in reality.

Question 10

The cost of data storage has plummeted recently,making data mining feasible for more firms.

Question 11

Using data mining on data about imports and exports can help to detect tax avoidance and money laundering.

Question 12

In the 2degrees case study,the main effectiveness of the new analytics system was in dissuading potential churners from leaving the company.

Question 13

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

Question 14

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.

Question 15

In data mining,classification models help in prediction.

Question 16

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.

Question 17

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

Question 18

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.

Question 19

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.

Question 20

Data mining can be very useful in detecting patterns such as credit card fraud,but is of little help in improving sales.

Question 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

Question 22

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

Question 23

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.

Question 24

The data field "salary" can be best described as

A) nominal data.
B) interval data.
C) ordinal data.
D) ratio data.

Question 25

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.

Question 26

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.

Question 27

The data field "ethnic group" can be best described as

A) nominal data.
B) interval data.
C) ordinal data.
D) ratio data.

Question 28

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

A) classifications.
B) regressions.
C) associations.
D) computations.

Question 29

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

Question 30

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.

Question 31

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

Question 32

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

Question 33

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.

Question 34

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.

Question 35

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.

Question 36

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.

Question 37

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

Question 38

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

Question 39

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.

Question 40

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

A) associations
B) visualization
C) classification
D) clustering

Question 41

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.

Question 42

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.

Question 43

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.

Question 44

In the opening vignette,Cabela's uses SAS data mining tools to create ________ models to optimize customer selection for all customer contacts.

Question 45

In the terrorist funding case study,an observed price ________ may be related to income tax avoidance/evasion,money laundering,or terrorist financing.

Question 46

While prediction is largely experience and opinion based,________ is data and model based.

Question 47

Because of its successful application to retail business problems,association rule mining is commonly called ________.

Question 48

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.

Question 49

Knowledge extraction,pattern analysis,data archaeology,information harvesting,pattern searching,and data dredging are all alternative names for ________.

Question 50

Fayyad et al.(1996)defined ________ in databases as a process of using data mining methods to find useful information and patterns in the data.

Question 51

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.

Question 52

Whereas ________ starts with a well-defined proposition and hypothesis,data mining starts with a loosely defined discovery statement.

Question 53

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

Question 54

Data preparation,the third step in the CRISP-DM data mining process,is more commonly known as ________.

Question 55

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

Question 56

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.

Question 57

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

Question 58

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.

Question 59

As described in the 2degrees case study,a common problem in the mobile telecommunications industry is defined by the term ________,which means customers leaving.

Question 60

________ 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.

Question 61

List five reasons for the growing popularity of data mining in the business world.

Question 62

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

Question 63

Describe the role of the simple split in estimating the accuracy of classification models.

Question 64

Briefly describe five techniques (or algorithms)that are used for classification modeling.

Question 65

List four myths associated with data mining.

Question 66

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

Question 67

Describe cluster analysis and some of its applications.

Question 68

List six common data mining mistakes.

Question 69

What are the differences between nominal,ordinal,interval and ratio data? Give examples.

Question 70

In lessons learned from the Target case,what legal warnings would you give another retailer using data mining for marketing?