Quizzes - Results 3 415
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School
California State University, Fullerton *
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Course
415
Subject
Information Systems
Date
Dec 6, 2023
Type
Pages
7
Uploaded by ProfessorGull3419
Results
Sruthi Barigela — 1st Attempt
22
Out of 24 points
17:43
Time for this attempt
1 attempt left
Your Answers:
1 / 3 points
Some myths associated with data mining are
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Attempt History
Results
Points
Score
(Highest score is kept)
Attempt 1
22 of 24
91.67%
(Highest score)
Data mining requires a
separate,
crystal-ball-like
predictions.
91.67%
1
1 / 1 point
Which of the following is a data mining myth?
1 / 1 point
In data mining, classification models help in prediction.
1 / 1 point
Data are often buried deep within very large
databases
, which
sometimes contain data from several years.
Data mining is not yet viable for
business applications.
Data mining provides instant,
dedicated database.
Newer Web-based tools enable managers of all educational levels to do data
mining.
Data mining is a multistep process that requires deliberate, proactive design
and use.
The current state-of-the-art is ready to go for almost any business.
Data mining requires a separate, dedicated database.
True
False
2
3
4
1 / 1 point
Clustering partitions a collection of things into segments whose members share
1 / 1 point
Prediction problems where the variables have numeric values are most accurately
defined as
1 / 1 point
When a problem has many attributes that impact the classification of different
patterns, decision trees may be a useful approach.
dissimilar characteristics.
similar characteristics.
similar collection methods.
dissimilar collection methods.
computations.
regressions.
associations.
correlations
classifications.
True
False
5
6
7
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1 / 1 point
Customer
relationship
management extends traditional marketing by
creating one-on-one relationships with customers.
1 / 1 point
Which data mining process/methodology is thought to be the most comprehensive,
according to kdnuggets.com rankings?
1 / 1 point
Knowledge extraction, pattern analysis, data archaeology, information harvesting,
pattern searching, and data dredging are all alternative names for
data mining
.
1 / 1 point
Fayyad et al. (1996) defined in
knowledge discovery
databases as a
process of using data mining methods to find useful information and patterns in the
data.
proprietary organizational methodologies
KDD Process
SEMMA
CRISP-DM
my own
8
9
10
11
2 / 2 points
Which broad area of data mining applications
1 / 1 point
The cost of data storage has plummeted recently, making data mining feasible for
more firms.
1 / 1 point
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.
1 / 1 point
partitions a collection of objects
into natural groupings with
similar features?
Clustering
analyzes data, forming rules to
distinguish between defined
classes?
Classification
True
False
True
False
12
13
14
15
A data mining study is specific to addressing a well-defined business task, and
different business tasks require
1 / 1 point
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.
1 / 1 point
Data mining requires specialized data analysts to ask ad hoc questions and obtain
answers quickly from the system.
2 / 2 points
relational data
general organizational data.
general industry data.
general economic data.
different sets of data.
True
False
True
False
16
17
18
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There has been an increase in data mining to deal with
global
competition and customers' more sophisticated
needs
and wants.
1 / 1 point
While prediction is largely experience and opinion based,
forecasting
is
data and model based.
1 / 1 point
What does the scalability of a data mining method refer to?
Its ability to predict the outcome of a previously unknown data set accurately.
Its ability to overcome noisy data to make somewhat accurate predictions.
Its speed of computation and computational costs in using the mode.
Its ability to construct a prediction model efficiently given a large
amount of data.
19
20