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

Question 1

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

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

Answer 1

Data mining.

Question 2

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

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

Answer 2

unstructured

Question 3

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 3

XML data is mostly tree structured

Question 4

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

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

Question 5

Following is not classification algorithm

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

Question 6

A common tool kit used for classification is__________

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

Question 7

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

Question 8

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

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

Question 9

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

Question 10

Supervised approaches depend on some a-priori knowledge of the data which are___________

A)Class ids
B)Class labels
C)Classifiers
D)None

Question 11

Clustering is a common____________ data mining technique

A)unsupervised
B)Supervised
C)both a & b
D)None of these

Question 12

Following is not a mining technique.

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

Question 13

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

Question 14

The out put of KDD is____________

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

Question 15

________________ 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

Question 16

Strategic value of data mining is____________

A)cost-sensitive
B)work-sensitive
C)time-sensitive
D)technique-sensitive

Question 17

_______________ 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

Question 18

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

Question 19

Self-organizing maps are an example of____________

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

Question 20

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

Question 21

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

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

Question 22

Eigenvector centrality takes eigen vector of ____________

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

Question 23

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

Question 24

__________algorithm is more effective for betweenness centrality.

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

Question 25

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