Question 1

Eigenvector centrality takes eigen vector of ____________

Accepted Answer

A) adjacency matrix 
B) Neighbouring matrix 
C) polling matrix 
D) All of Above 
A) adjacency matrix 
B) Neighbouring matrix 
C) polling matrix 
D) All of Above A

Question 2

The________ centrality measure does not allow for centrality values to be compared across networks

Accepted Answer

A) Eigenvector 
B) Katz 
C) degree 
D) None 
A) Eigenvector 
B) Katz 
C) degree 
D) None C

Question 3

Following is not a mining technique.

Accepted Answer

A) Bayesian classification 
B) rule-based classifier 
C) support vector machines, 
D) ObjectRanking 
A) Bayesian classification 
B) rule-based classifier 
C) support vector machines, 
D) ObjectRanking D

Question 4

__________algorithm is more effective for betweenness centrality.

Accepted Answer

A) adjacency matrix 
B) Dijkstra\s 
C) Neighbouring matrix 
D) Brandes\ 
A) adjacency matrix 
B) Dijkstra\s 
C) Neighbouring matrix 
D) Brandes\

Question 5

A common tool kit used for classification is__________

Accepted Answer

A) Bridges 
B) Rainbow 
C) Naive Bayes 
D) TFIDF 
A) Bridges 
B) Rainbow 
C) Naive Bayes 
D) TFIDF

Question 6

Keyword search on XML data is a simpler problem because_______

Accepted Answer

A) XML data is mostly not structured 
B) XML data is mostly tree structured 
C) XML data is mostly semi structured 
D) XML data is mostly fully structured 
A) XML data is mostly not structured 
B) XML data is mostly tree structured 
C) XML data is mostly semi structured 
D) XML data is mostly fully structured

Question 7

Following is not classification algorithm

Accepted Answer

A) Naive Bayes 
B) TFIDF 
C) Probabilistic Indexing 
D) Indexbased 
A) Naive Bayes 
B) TFIDF 
C) Probabilistic Indexing 
D) Indexbased

Question 8

Text mining reads an ____________ form of data to provide meaningful information patterns

Accepted Answer

A) structured 
B) unstructured 
C) semistructured 
D) None of Above 
A) structured 
B) unstructured 
C) semistructured 
D) None of Above

Question 9

The primary idea in___________ is that data mining problems have varying levels of diffculty in different domains

Accepted Answer

A) clustering 
B) classification 
C) transfer learning 
D) keyword search 
A) clustering 
B) classification 
C) transfer learning 
D) keyword search

Question 10

_______________ is a summarization of the general characteristics or features of a target class of data.

Accepted Answer

A) Data Classification 
B) Data discrimination 
C) Data selection 
D) Data Characterization 
A) Data Classification 
B) Data discrimination 
C) Data selection 
D) Data Characterization

Question 11

____________predicts future trends & behaviors, allowing business managers to make proactive,knowledge-driven decisions.

Accepted Answer

A) Data warehouse. 
B) Datamarts 
C) Data mining. 
D) Metadata 
A) Data warehouse. 
B) Datamarts 
C) Data mining. 
D) Metadata

Question 12

The problem of network clustering is closely related to the traditional problem of ___________

Accepted Answer

A) edge partitioning 
B) node partitioning 
C) graph partitioning 
D) vector partitioning 
A) edge partitioning 
B) node partitioning 
C) graph partitioning 
D) vector partitioning

Question 13

Some telecommunication company wants to segment their customers into distinct groups in order to send appropriate subscription offers, this is an example of_______

Accepted Answer

A) Supervised learning 
B) Data extraction 
C) Serration 
D) Unsupervised learning 
A) Supervised learning 
B) Data extraction 
C) Serration 
D) Unsupervised learning

Question 14

Most well-known keyword search algorithm for relational data is _______

Accepted Answer

A) DBX-plorer 
B) DISCOVER 
C) both a & b 
D) None of these 
A) DBX-plorer 
B) DISCOVER 
C) both a & b 
D) None of these

Question 15

When bias term is added to the centrality values for all nodes no matter how they are situated in the network it is called_______

Accepted Answer

A) Eigenvector 
B) Katz 
C) degree 
D) None 
A) Eigenvector 
B) Katz 
C) degree 
D) None

Question 16

Bayesian classifiers is____________

Accepted Answer

A) A class of learning algorithm that tries to find an optimum classification of a set of examples using the probabilistic theory. 
B) Any mechanism employed by a learning system to constrain the search space of a hypothesis 
C) An approach to the design of learning algorithms that is inspired by the fact that when people encounter new situations, they often explain them by reference to familiar experiences, adapting the explanations to fit the new situation. 
D) None of these 
A) A class of learning algorithm that tries to find an optimum classification of a set of examples using the probabilistic theory. 
B) Any mechanism employed by a learning system to constrain the search space of a hypothesis 
C) An approach to the design of learning algorithms that is inspired by the fact that when people encounter new situations, they often explain them by reference to familiar experiences, adapting the explanations to fit the new situation. 
D) None of these

Question 17

Major challenge which arises in the context of social networks is that many such networks are______________

Accepted Answer

A) homogeneous 
B) heterogeneous 
C) unstructured 
D) semistructured 
A) homogeneous 
B) heterogeneous 
C) unstructured 
D) semistructured

Question 18

________________ is the process of finding a model that describes and distinguishes data classes or concepts.

Accepted Answer

A) Data Characterization 
B) Data Classification 
C) Data discrimination 
D) Data selection 
A) Data Characterization 
B) Data Classification 
C) Data discrimination 
D) Data selection

Question 19

Self-organizing maps are an example of____________

Accepted Answer

A) Unsupervised learning 
B) Supervised learning 
C) Reinforcement learning 
D) Missing data imputation 
A) Unsupervised learning 
B) Supervised learning 
C) Reinforcement learning 
D) Missing data imputation

Question 20

The out put of KDD is____________

Accepted Answer

A) Data 
B) Information 
C) Query 
D) Useful information 
A) Data 
B) Information 
C) Query 
D) Useful information

Eigenvector centrality takes eigen vector of ____________

The________ centrality measure does not allow for centrality values to be compared across networks

Following is not a mining technique.

__________algorithm is more effective for betweenness centrality.

A common tool kit used for classification is__________

Keyword search on XML data is a simpler problem because_______

Following is not classification algorithm

Text mining reads an ____________ form of data to provide meaningful information patterns

The primary idea in___________ is that data mining problems have varying levels of diffculty in different domains

_______________ is a summarization of the general characteristics or features of a target class of data.

____________predicts future trends & behaviors, allowing business managers to make proactive,knowledge-driven decisions.

The problem of network clustering is closely related to the traditional problem of ___________

Some telecommunication company wants to segment their customers into distinct groups in order to send appropriate subscription offers, this is an example of_______

Most well-known keyword search algorithm for relational data is _______

When bias term is added to the centrality values for all nodes no matter how they are situated in the network it is called_______

Bayesian classifiers is____________

Major challenge which arises in the context of social networks is that many such networks are______________

________________ is the process of finding a model that describes and distinguishes data classes or concepts.

Self-organizing maps are an example of____________

The out put of KDD is____________

Media, Analytics, and Data Mining Techniques

Social Network Analysis and Clustering Algorithms

Network Analysis and Behavior Modeling in Social Media

Exploring User Migration, Security, and Data Mining in Social Media Sites

Data Mining and Clustering: Key Concepts and Tasks

Social Media and Networking

Exploring Network Visualization and Analysis Tools: Techniques, Measures, and Applications

Network Analysis and Individual Behavior: Understanding Centrality, Transitivity, and Social Dynamics

Network Analysis and Social Media

Social Media Analytics and Data Extraction

Social Media Analysis and Modeling Techniques

Various Aspects of Social Media and Prediction Techniques

Filters

Exam 8: Data Mining and Network Analysis: Key Concepts and Techniques

Eigenvector centrality takes eigen vector of ____________

The________ centrality measure does not allow for centrality values to be compared across networks

Following is not a mining technique.

__________algorithm is more effective for betweenness centrality.

A common tool kit used for classification is__________

Keyword search on XML data is a simpler problem because_______

Following is not classification algorithm

Text mining reads an ____________ form of data to provide meaningful information patterns

The primary idea in___________ is that data mining problems have varying levels of diffculty in different domains

_______________ is a summarization of the general characteristics or features of a target class of data.

____________predicts future trends & behaviors, allowing business managers to make proactive,knowledge-driven decisions.

The problem of network clustering is closely related to the traditional problem of ___________

Some telecommunication company wants to segment their customers into distinct groups in order to send appropriate subscription offers, this is an example of_______

Most well-known keyword search algorithm for relational data is _______

When bias term is added to the centrality values for all nodes no matter how they are situated in the network it is called_______

Bayesian classifiers is____________

Major challenge which arises in the context of social networks is that many such networks are______________

________________ is the process of finding a model that describes and distinguishes data classes or concepts.

Self-organizing maps are an example of____________

The out put of KDD is____________

Media, Analytics, and Data Mining Techniques

Social Network Analysis and Clustering Algorithms

Network Analysis and Behavior Modeling in Social Media

Exploring User Migration, Security, and Data Mining in Social Media Sites

Data Mining and Clustering: Key Concepts and Tasks

Social Media and Networking

Exploring Network Visualization and Analysis Tools: Techniques, Measures, and Applications

Network Analysis and Individual Behavior: Understanding Centrality, Transitivity, and Social Dynamics

Network Analysis and Social Media

Social Media Analytics and Data Extraction

Social Media Analysis and Modeling Techniques

Various Aspects of Social Media and Prediction Techniques

Filters