Contents
Classification in Data Mining MCQ and Answers
These Classification in Data Mining MCQ and Answers are composed by our Livemcqs Team. Below we also provide some most important multiple choice questions on Data Mining that are asked frequently in the examinations.
1. 26. Data mining 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
2. 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
3. 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
4. Self-organizing maps
are an example of
A.
Unsupervised learning
B. Supervised learning
C. Reinforcement learning
D. Missing data imputation
5. You are given data
about seismic activity in Japan, and you want to predict a magnitude of the
next earthquake, this is in an example of
A.
Supervised learning
B. Unsupervised learning
C. Serration
D. Dimensionality reduction
6. In the example of
predicting number of babies based on storks’ population size, number of babies
is
A.
outcome
B. feature
C. attribute
D. observation
7. Which of the following
issue is considered before investing in Data Mining?
A. Functionality
B. Vendor consideration
C. Compatibility
D. All of the above
8. 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
9. 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
10. 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
11. Data independence
means
A. Data is defined separately and not included in
programs
B. Programs are not dependent on the physical attributes of data.
C. Programs are not dependent on the logical attributes of data
D. Both (B) and (C).
12. E-R model uses this
symbol to represent weak entity set?
A. Dotted rectangle
B. Diamond
C. Doubly outlined rectangle
D. None of these
13. SET concept is used
in
A. Network Model
B. Hierarchical Model
C. Relational Model
D. None of these
14. Relational Algebra is
A. Data Definition Language
B. Meta Language
C. Procedural query Language
D. None of the above
15. 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
More Related MCQs on Data Mining
What is classification MCQ data mining?
The term “classification” refers to the classification of the given data into certain sub-classes or groups according to their similarities or on the basis of the specific given set of rules.
What is classification in MCQ?
Classification is a subdivision of a set of examples into a number of classes. A measure of the accuracy, of the classification of a concept that is given by a certain theory.
What is data mining MCQ function?
Data mining is a process of extracting and discovering patterns in large data sets. B. Data mining is the process of finding correlations within large data sets.
What is data classification in machine learning?
Classification is a process of categorizing a given set of data into classes, It can be performed on both structured or unstructured data. The process starts with predicting the class of given data points. The classes are often referred to as target, label or categories.