CSC 240 Quiz 1 (1)
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University of Rochester *
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Course
240
Subject
Information Systems
Date
Apr 3, 2024
Type
Pages
8
Uploaded by BaronLightningKookabura16
What is the difference between classification and regression?
The former refers to a comparison of the general features of target class data objects with the
general features of objects from one or a set of contrasting
classes, while the latter is the process of finding a set of models (or functions) that describe
and distinguish data classes or concepts for the purpose of being able to use the model to
predict the class of objects whose class label is unknown.
The former refers to a summarization of the general characteristics or features of a target class
of data while the latter deals with the analysis of data objects without consulting a known class
label.
The former predicts categorical (discrete, unordered) labels while the latter predicts missing or
unavailable, and often numerical, data values.
There is no difference, these concepts are synonymous.
Confirmatory data analysis is sometimes called inferential statistics
True
False
DBMS stands for
Data Before Millenial Systems
Data Base Masters of Science
Direct Biometric Manipulation Switch
Database Management System
4. Transactional data is most typically represented using
A string
A hash table
A list
A graph
What is the difference between discrimination and classification?
The former refers to a comparison of the general features of target class data objects with the
general features of objects from one or a set of contrasting
classes, while the latter is the process of finding a set of models (or functions) that describe
and distinguish data classes or concepts for the purpose of being able to use the model to
predict the class of objects whose class label is unknown.
The former refers to a summarization of the general characteristics or features of a target class
of data while the latter deals with the analysis of data objects without consulting a known class
label.
The former predicts categorical (discrete, unordered) labels while the latter predicts missing or
unavailable, and often numerical, data values.
There is no difference, these concepts are synonymous.
Regression, unlike classification, is a process to model continuous-valued functions.
True
False
Who is the mother of invention?
Genius
Education
Necessity
Investment
Which of the following is NOT typically considered a step in the iterative knowledge discovery process?
Data cleaning
Data integration
Data funding
Data selection
Data transformation
Data mining
Pattern evaluation
Knowledge presentation
In data mining, outliers are considered to be uninteresting noise that need to be removed during the data
cleaning step.
True
False
BI stands for
Binary Integration
Biological Inception
Business intelligence
Biased Inference
What is the difference between characterization and clustering?
The former refers to a comparison of the general features of target class data objects with the
general features of objects from one or a set of contrasting
classes, while the latter is the process of finding a set of models (or functions) that describe
and distinguish data classes or concepts for the purpose of being able to use the model to
predict the class of objects whose class label is unknown.
The former refers to a summarization of the general characteristics or features of a target class
of data while the latter deals with the analysis of data objects without consulting a known class
label.
The former predicts categorical (discrete, unordered) labels while the latter predicts missing or
unavailable, and often numerical, data values.
There is no difference, these concepts are synonymous.
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IR stands for
Infra Red
Inter Regional
Iterative Regression
Information retrieval
KDD stands for
Knowledge Discovery from Data
Knowing Data Directly
Kinematic Disclosure Device
Keeping Dangerous Data
OLAP stands for
Ordinary Layered Associative Processing
Object Latency Allocation Protocol
Online Analytical Processing
Occasional Late Acceptance Policy
By tracking search terms, Google's Flu Trends can estimate flu activity faster than traditional system.
True
False
In predictive analysis, the model typified by IF-THEN rules is most commonly called
A Neural Network
A Support Vector Machine
A Hidden Markov Model
A Decision Tree
OLAP is considered part of
Artificial
Intelligence
Pattern
recognition
Database
Management
Systems
Data warehouse
systems
Inferential statistics are sometimes called predictive data analysis.
True
False
A table consists of
a set of attributes and a set of tuples
a set of objects and a set of indicies
a set of concepts and a set of designs
a set of four legs and a flat top
Most patterns are interesting
True
False
A relational database is a collection of
records
attributes
tables
objects
In frequent pattern mining association analysis invovles two important metrics
span and depth
confidence and support
central tendency and dispersion
mean and standard deviation
A petabyte is equal to
1 million gigabytes
1 million terabytes
1 million kilobytes
1 million bytes
Match the machine learning term with an appropriate concept
-
A.
B.
C.
D.
Supervised learning
-
A.
B.
C.
D.
Unsupervised learning
A.
clustering
B.
involving labeled and unlabeled data
C.
classification
D.
involving human users
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-
A.
B
.
C.
D.
semi-supervised learning
-
A.
B.
C.
D
.
Active learning
One well known conceptual tool used to understand mult-resolution multi-dimensional data in a data
warhouse is the
Data cloud
Data sphere
Data pyramid
Data cube
Data mining functionalities can be classified into two categories
proceedural and functional
objective and relational
descriptive and predictive
top-down and bottom-up
Two well known OLAP operations are
bottom up and top down
roll up and drill down
send up and transfer down
speed up and slow down
What are the major challenges of mining a huge amount of data (such as billions of tuples) in comparison
with mining a small amount of data (such as a few hundred tuple data set)?
social impacts and privacy
interactivity and visualization
efficiency and scalability
abstraction and generalization
In the evolution of database system technology, the era of Database Management Systems was/is
1960's and earlier
1970's to early 1980s
mid 1980s to present
late 1980s to present
In Cluster analysis, the goal is to
maximize intraclass similarity and maximize interclass similarity
minimize intraclass similarity and maximize interclass similarity
maximize intraclass similarity and minimize interclass similarity
minimize intraclass similarity and minimize interclass similarity