McqMate
These multiple-choice questions (MCQs) are designed to enhance your knowledge and understanding in the following areas: Information Technology Engineering (IT) .
| 151. |
Popular algorithm for node_link diagram is ___________ |
| A. | Link state routing algorithm |
| B. | Forced directed layout algorithm |
| C. | Semantic based layout algorithm |
| D. | None of Above |
| Answer» B. Forced directed layout algorithm | |
| 152. |
Visualization is ______display of data. |
| A. | pictorial |
| B. | graphical |
| C. | numerical |
| D. | linear |
| Answer» B. graphical | |
| 153. |
Social network analysis is process of investigating through use of ____and ______ |
| A. | Edges, Graph |
| B. | Vector,graph |
| C. | network , Graph |
| D. | Vector, Edges |
| Answer» C. network , Graph | |
| 154. |
Adjacency graphs are ___________ |
| A. | one mode |
| B. | two mode |
| C. | bipertite |
| D. | None of Above |
| Answer» A. one mode | |
| 155. |
Following are measures of Centrality |
| A. | degree |
| B. | Betweenness |
| C. | Eigen vector |
| D. | All above |
| Answer» D. All above | |
| 156. |
Current visualization of social n/w is some form of ____________ |
| A. | Node_Link_diagram |
| B. | Vector Diagram |
| C. | Edge Diagram |
| D. | All above |
| Answer» A. Node_Link_diagram | |
| 157. |
Which is not algorithmic approach for visualization? |
| A. | Tree layout |
| B. | Layered layout |
| C. | Symmetric layout |
| D. | Edge Layout |
| Answer» D. Edge Layout | |
| 158. |
Hive plots defines____________layout for nodes |
| A. | Linear |
| B. | graphical |
| C. | structural |
| D. | vectorial |
| Answer» A. Linear | |
| 159. |
Circular graph layout is a drawing scheme where all nodes are placed on the ____________ of a circle. |
| A. | area |
| B. | perimeter |
| C. | radius |
| D. | diagonal |
| Answer» B. perimeter | |
| 160. |
A measure of connectedness between components of graph is ___________ |
| A. | degree |
| B. | closeness |
| C. | Eigen value |
| D. | Betweenness |
| Answer» D. Betweenness | |
| 161. |
Following are N/w Visualization applications. |
| A. | Moviegalaxies |
| B. | Twitterscope |
| C. | LinkedIn Maps |
| D. | All above |
| Answer» D. All above | |
| 162. |
Affiliation graphs are ___________ |
| A. | one mode |
| B. | two mode |
| C. | multimode |
| D. | None of Above |
| Answer» B. two mode | |
| 163. |
Which is not a visualization tool |
| A. | NodeXL |
| B. | Ruby |
| C. | Pajek |
| D. | Gephi |
| Answer» B. Ruby | |
| 164. |
____________ is an open source software platform for visualizing molecular interaction networks |
| A. | Cytoscape |
| B. | Cuttlefish |
| C. | Commetrix |
| D. | Egonet |
| Answer» A. Cytoscape | |
| 165. |
Software Framework for Dynamic Network Visualization and Analysis is ______ |
| A. | Cytoscape |
| B. | Cuttlefish |
| C. | Commetrix |
| D. | Egonet |
| Answer» C. Commetrix | |
| 166. |
____________is a cloud-based text and social networks analyzer |
| A. | Cytoscape |
| B. | Gephi |
| C. | Pajek |
| D. | Netlytic |
| Answer» D. Netlytic | |
| 167. |
_______performs network analysis AND network visualization in one integrated product. |
| A. | Cytoscape |
| B. | InFlow |
| C. | Gephi |
| D. | Egonet |
| Answer» B. InFlow | |
| 168. |
MeerKat offers facilities for automated ___________mining |
| A. | data |
| B. | vector |
| C. | community |
| D. | cluster |
| Answer» C. community | |
| 169. |
____________ is dedicated to the analysis and visualization of relational data. |
| A. | Tulip |
| B. | InFlow |
| C. | Gephi |
| D. | Egonet |
| Answer» A. Tulip | |
| 170. |
___________is an interactive visualization and exploration platform for all kinds of networks and complex systems, dynamic and hierarchical graphs |
| A. | Cytoscape |
| B. | Gephi |
| C. | Commetrix |
| D. | Cuttlefish |
| Answer» B. Gephi | |
| 171. |
_______software for analysis and visualization of large networks used for Windows-only. |
| A. | NodeXL |
| B. | Ruby |
| C. | Pajek |
| D. | Gephi |
| Answer» C. Pajek | |
| 172. |
________is used for bibliometric data. |
| A. | VOSViewer |
| B. | Gephi |
| C. | Commetrix |
| D. | Cuttlefish |
| Answer» A. VOSViewer | |
| 173. |
_______is an online tool that lets you visualize and analyze LinkedIn network |
| A. | Cytoscape |
| B. | Gephi |
| C. | Commetrix |
| D. | Socilab |
| Answer» D. Socilab | |
| 174. |
________________is cross-platform user friendly tool that allows you to draw social network |
| A. | VOSViewer |
| B. | Social Network Visualizer |
| C. | Commetrix |
| D. | Cuttlefish |
| Answer» B. Social Network Visualizer | |
| 175. |
___________is open source and free visualization tool |
| A. | NodeXL |
| B. | Ruby |
| C. | Pajek |
| D. | Gephi |
| Answer» D. Gephi | |
| 176. |
____________predicts future trends & behaviors, allowing business managers to make proactive,knowledge-driven decisions. |
| A. | Data warehouse. |
| B. | Datamarts |
| C. | Data mining. |
| D. | Metadata |
| Answer» C. Data mining. | |
| 177. |
Text mining reads an ____________ form of data to provide meaningful information patterns |
| A. | structured |
| B. | unstructured |
| C. | semistructured |
| D. | None of Above |
| Answer» B. unstructured | |
| 178. |
Keyword search on XML data is a simpler problem because_______ |
| 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 |
| Answer» B. XML data is mostly tree structured | |
| 179. |
Most well-known keyword search algorithm for relational data is _______ |
| A. | DBX-plorer |
| B. | DISCOVER |
| C. | Both |
| D. | None |
| Answer» C. Both | |
| 180. |
Following is not classification algorithm |
| A. | Naive Bayes |
| B. | TFIDF |
| C. | Probabilistic Indexing |
| D. | Indexbased |
| Answer» D. Indexbased | |
| 181. |
A common tool kit used for classification is__________ |
| A. | Bridges |
| B. | Rainbow |
| C. | Naive Bayes |
| D. | TFIDF |
| Answer» B. Rainbow | |
| 182. |
The problem of network clustering is closely related to the traditional problem of ___________ |
| A. | edge partitioning |
| B. | node partitioning |
| C. | graph partitioning |
| D. | vector partitioning |
| Answer» C. graph partitioning | |
| 183. |
Major challenge which arises in the context of social networks is that many such networks are______________ |
| A. | homogeneous |
| B. | heterogeneous |
| C. | unstructured |
| D. | semistructured |
| Answer» B. heterogeneous | |
| 184. |
The primary idea in___________ is that data mining problems have varying levels of diffculty in different domains |
| A. | clustering |
| B. | classification |
| C. | transfer learning |
| D. | keyword search |
| Answer» C. transfer learning | |
| 185. |
Supervised approaches depend on some a-priori knowledge of the data which are___________ |
| A. | Class ids |
| B. | Class labels |
| C. | Classifiers |
| D. | None |
| Answer» B. Class labels | |
| 186. |
Clustering is a common____________ data mining technique |
| A. | unsupervised |
| B. | Supervised |
| C. | both |
| D. | None |
| Answer» A. unsupervised | |
| 187. |
Following is not a mining technique. |
| A. | Bayesian classification |
| B. | rule-based classifier |
| C. | support vector machines, |
| D. | ObjectRanking |
| Answer» D. ObjectRanking | |
| 188. |
Which of the following is not a data mining functionality?
|
| A. | Characterization and Discrimination |
| B. | Classification and regression |
| C. | Selection and interpretation |
| D. | Clustering and Analysis |
| Answer» C. Selection and interpretation | |
| 189. |
The out put of KDD is____________
|
| A. | Data |
| B. | Information |
| C. | Query |
| D. | Useful information |
| Answer» D. Useful information | |
| 190. |
________________ is the process of finding a model that describes and distinguishes data classes or concepts.
|
| A. | Data Characterization |
| B. | Data Classification |
| C. | Data discrimination |
| D. | Data selection |
| Answer» B. Data Classification | |
| 191. |
Strategic value of data mining is____________ |
| A. | cost-sensitive |
| B. | work-sensitive |
| C. | time-sensitive |
| D. | technique-sensitive |
| Answer» C. time-sensitive | |
| 192. |
_______________ is a summarization of the general characteristics or features of a target class of data. |
| A. | Data Classification |
| B. | Data discrimination |
| C. | Data selection |
| D. | Data Characterization |
| Answer» D. Data Characterization | |
| 193. |
Bayesian classifiers is____________ |
| 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 |
| Answer» A. A class of learning algorithm that tries to find an optimum classification of a set of examples using the probabilistic theory. | |
| 194. |
Self-organizing maps are an example of____________
|
| A. | Unsupervised learning |
| B. | Supervised learning |
| C. | Reinforcement learning |
| D. | Missing data imputation |
| Answer» A. Unsupervised learning | |
| 195. |
Some telecommunication company wants to segment their customers into distinct groups in order to send appropriate subscription offers, this is an example of_______ |
| A. | Supervised learning |
| B. | Data extraction |
| C. | Serration |
| D. | Unsupervised learning |
| Answer» D. Unsupervised learning | |
| 196. |
The________ centrality measure does not allow for centrality values to be compared across networks |
| A. | Eigenvector |
| B. | Katz |
| C. | degree |
| D. | None |
| Answer» C. degree | |
| 197. |
Eigenvector centrality takes eigen vector of ____________ |
| A. | adjacency matrix |
| B. | Neighbouring matrix |
| C. | polling matrix |
| D. | All of Above |
| Answer» A. adjacency matrix | |
| 198. |
When bias term is added to the centrality values for all nodes no matter how they are situated in the network it is called_______ |
| A. | Eigenvector |
| B. | Katz |
| C. | degree |
| D. | None |
| Answer» B. Katz | |
| 199. |
__________algorithm is more effective for betweenness centrality. |
| A. | adjacency matrix |
| B. | Dijkstra\s |
| C. | Neighbouring matrix |
| D. | Brandes\ |
| Answer» D. Brandes\ | |
| 200. |
In____________centrality, the intuition is that the more central nodes are, the more quickly they can reach other nodes. |
| A. | Eigenvector |
| B. | Katz |
| C. | Closeness |
| D. | degree |
| Answer» C. Closeness | |
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