Data Mining solved MCQs

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1. Adaptive system management is

a. it uses machine-learning techniques. here program can learn from past experience and adapt themselves to new situations.

B. computational procedure that takes some value as input and produces some value as output.

c. science of making machines performs tasks that would require intelligence when performed by humans.

d. none of these

2. 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

3. Algorithm is

a. it uses machine-learning techniques. here program can learn from past experience and adapt themselves to new situations.

B. computational procedure that takes some value as input and produces some value as output.

c. science of making machines performs tasks that would require intelligence when performed by humans.

d. none of these

4. Bias 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

5. Background knowledge referred to

a. additional acquaintance used by a learning algorithm to facilitate the learning process.

B. a neural network that makes use of a hidden layer.

c. it is a form of automatic learning.

d. none of these

6. Case-based learning 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

7. Classification is

a. a subdivision of a set of examples into a number of classes.

B. a measure of the accuracy, of the classification of a concept that is given by a certain theory.

c. the task of assigning a classification to a set of examples

d. none of these

8. Binary attribute are

a. this takes only two values. in general, these values will be 0 and 1 and .they can be coded as one bit

B. the natural environment of a certain species.

c. systems that can be used without knowledge of internal operations.

d. none of these

9. Classification accuracy is

a. a subdivision of a set of examples into a number of classes

B. measure of the accuracy, of the classification of a concept that is given by a certain theory.

c. the task of assigning a classification to a set of examples

d. none of these

10. Biotope are

a. this takes only two values. in general, these values will be 0 and 1 and they can be coded as one bit.

B. the natural environment of a certain species

c. systems that can be used without knowledge of internal operations

d. none of these

11. Cluster is

a. group of similar objects that differ significantly from other objects

B. operations on a database to transform or simplify data in order to prepare it for a machine-learning algorithm

c. symbolic representation of facts or ideas from which information can potentially be extracted

d. none of these

12. Black boxes are

a. this takes only two values. in general, these values will be 0 and 1 and they can be coded as one bit.

B. the natural environment of a certain species

c. systems that can be used without knowledge of internal operations

d. none of these

13. A definition of a concept is-----if it recognizes all the instances of that concept

a. complete

B. consistent

c. constant

d. none of these

14. A definition or a concept is------------- if it classifies any examples as coming within the concept

a. complete

B. consistent

c. constant

d. none of these

15. Data selection is

a. the actual discovery phase of a knowledge discovery process

B. the stage of selecting the right data for a kdd process

c. a subject-oriented integrated time variant non-volatile collection of data in support of management

d. none of these

16. DNA (Deoxyribonucleic acid)

a. it is hidden within a database and can only be recovered if one ,is given certain clues (an example is encrypted information).

B. the process of executing implicit previously unknown and potentially useful information from data

c. an extremely complex molecule that occurs in human chromosomes and that carries genetic information in the form of genes.

d. none of these

17. Hybrid is

a. combining different types of method or information

B. approach to the design of learning algorithms that is structured along the lines of the theory of evolution.

c. decision support systems that contain an information base filled with the knowledge of an expert formulated in terms of if-then rules.

d. none of these

18. Discovery is

a. it is hidden within a database and can only be recovered if one is given certain clues (an example is encrypted information).

B. the process of executing implicit previously unknown and potentially useful information from data.

c. an extremely complex molecule that occurs in human chromosomes and that carries genetic information in the form of genes.

d. none of these

19. Euclidean distance measure is

a. a stage of the kdd process in which new data is added to the existing selection.

B. the process of finding a solution for a problem simply by enumerating all possible solutions according to some pre-defined order and then testing them

c. the distance between two points as calculated using the pythagoras theorem.

d. none of these

20. Hidden knowledge referred to

a. a set of databases from different vendors, possibly using different database paradigms

B. an approach to a problem that is not guaranteed to work but performs well in most cases

c. information that is hidden in a database and that cannot be recovered by a simple sql query.

d. none of these

21. Enrichment is

a. a stage of the kdd process in which new data is added to the existing selection

B. the process of finding a solution for a problem simply by enumerating all possible solutions according to some pre-defined order and then testing them

c. the distance between two points as calculated using the pythagoras theorem.

d. none of these

22. Heterogeneous databases referred to

a. a set of databases from different b vendors, possibly using different database paradigms

B. an approach to a problem that is not guaranteed to work but performs well in most cases.

c. information that is hidden in a database and that cannot be recovered by a simple sql query.

d. none of these

23. Enumeration is referred to

a. a stage of the kdd process in which new data is added to the existing selection.

B. the process of finding a solution for a problem simply by enumerating all possible solutions according to some pre-defined order and then testing them

c. the distance between two points as calculated using the pythagoras theorem.

d. none of these

24. Heuristic is

a. a set of databases from different vendors, possibly using different database paradigms

B. an approach to a problem that is not guaranteed to work but performs well in most cases

c. information that is hidden in a database and that cannot be recovered by a simple sql query.

d. none of these

25. Hybrid learning is

a. machine-learning involving different techniques

B. the learning algorithmic analyzes the examples on a systematic basis 2nd makes incremental adjustments to the theory that is learned

c. learning by generalizing from examples

d. none of these

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