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This set of Social Media Analytics (SMA) Multiple Choice Questions & Answers (MCQs) focuses on Social Media Analytics Set 8
Q1 | Text mining reads an ____________ form of data to provide meaningful information patterns
- structured
- unstructured
- semistructured
- None of Above
Q2 | Keyword search on XML data is a simpler problem because_______
- XML data is mostly not structured
- XML data is mostly tree structured
- XML data is mostly semi structured
- XML data is mostly fully structured
Q3 | Most well-known keyword search algorithm for relational data is _______
- DBX-plorer
- DISCOVER
- Both
- None
Q4 | Following is not classification algorithm
- Naive Bayes
- TFIDF
- Probabilistic Indexing
- Indexbased
Q5 | A common tool kit used forclassification is__________
- Bridges
- Rainbow
- Naive Bayes
- TFIDF
Q6 | The problem of network clustering is closely related to the traditional problem of ___________
- edge partitioning
- node partitioning
- graph partitioning
- vector partitioning
Q7 | Supervised approaches depend on some a-priori knowledge ofthe data which are___________
- Class ids
- Class labels
- Classifiers
- None
Q8 | Following is not a mining technique.
- Bayesian classification
- rule-based classifier
- support vector machines,
- ObjectRanking
Q9 | Which of the following is not a data mining functionality?
- Characterization and Discrimination
- Classification and regression
- Selection and interpretation
- Clustering and Analysis
Q10 | The out put of KDD is____________
- Data
- Information
- Query
- Useful information
Q11 | Strategic value of data mining is____________
- cost-sensitive
- work-sensitive
- time-sensitive
- technique-sensitive
Q12 | _______________ is a summarization of the general characteristics or features of a target class of data.
- Data Classification
- Data discrimination
- Data selection
- Data Characterization
Q13 | Self-organizing maps are an example of____________
- Unsupervised learning
- Supervised learning
- Reinforcement learning
- Missing data imputation
Q14 | Some telecommunication company wants to segment their customers into distinct groups in order to send appropriate subscription offers, this is an example of_______
- Supervised learning
- Data extraction
- Serration
- Unsupervised learning
Q15 | When bias term is added to the centrality values for all nodes no matter how they are situated in the network it is called_______
- Eigenvector
- Katz
- degree
- None
Q16 | __________algorithm is more effective for betweenness centrality.
- adjacency matrix
- Dijkstra\s
- Neighbouring matrix
- Brandes\
Q17 | In____________centrality, the intuition is that the more central nodes are, themore quickly they can reach other nodes.
- Eigenvector
- Katz
- Closeness
- degree