Data Mining MCQ Multiple Choice Questions with answers for 2024

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Look no further, as you have stumbled upon the right place! In this article, we will provide you with a wide range of MCQs specifically tailored for Data Mining, enabling you to sharpen your knowledge and enhance your chances of success.

Whether you are a beginner or already well-versed in the subject matter, these questions will serve as a valuable resource to gauge your understanding and test your grasp on key concepts in Data Mining. So let’s dive into the realm of multiple choice questions and prepare ourselves thoroughly for the challenges that lie ahead!

Top 110 Data Mining MCQs with answers

1. Information can be converted into knowledge about ___ patterns and future trends.
Ans: Historical

2. Data about data is called ___.
Ans: Metadata

3. Facts, numbers, or text is called ___.
Ans: Data

4. ___ and ___ are the key to emerging Business Intelligence technologies.
Ans: Data warehouse and data mining

5. Data mining is also called ___.
Ans: Knowledge discovery

6. Online Analytical Processing (OLAP) is a technology that is used to create ___ software.
Ans: Decision support

7. OLAP Supports ___ user access and multiple queries.
Ans: Multiple

8. Statistics techniques are incorporated into Data mining methods. (True/False).
Ans: True

9. ___ Optimization techniques are based on the concepts of genetic combination, mutation, and natural selection.
Ans: Genetic algorithms

10. What is Mineset?
Ans: MineSet is software that provides tools for searching, sorting, filtering and drilling down enabling previously complex data models to be viewed intuitively through real-time 3-D graphical representation.

11. A data warehouse refers to a database that is maintained separately from an organization’s operational databases. (True/False)
Ans: True

12. A data warehouse is usually constructed by integrating multiple heterogeneous sources. (True/False)
Ans: True

13. ___ system is customer-oriented and is used for transaction and query processing by clerks, clients, and information technology professionals.
Ans: OLTP

14. A ___ allows data to be modelled and viewed in multiple Dimensions.
Ans: Data cube

15. In ___ schema some dimension tables are normalized, thereby further splitting the data into additional tables.
Ans: Snowflake

16. The ___ data model is commonly used in the design of relational databases.
Ans: Entity-relationship

17. Data warehouses and OLAP tools are based on ___ data model.
Ans: Multidimensional

18. The ___ exposes the information being captured, stored, and managed by operational systems.
Ans: Data source view

19. ___ are the intermediate servers that stand in between a relational back – end server and client front – end tools.
Ans: Relational OLAP (ROLAP) servers

20. A ___ is a set of views over operational databases.
Ans: Virtual warehouse

21. The ___ software gives the user the opportunity to look at the data from a variety of different dimensions.
Ans: Multidimensional Analysis

22. Which of the following statements defines Business Intelligence?
A. Converting data into knowledge and making it available throughout the organization
B. Analytical software and solutions for gathering, consolidating, analyzing and providing access to information in a way that is supposed to let the users of an enterprise make better business decisions.
C. Both A & B
Ans: C. Both A & B

23. Based on the overall requirements of business intelligence, the ___ layer is required to extract, cleanse and transform data into load files for the information warehouse.
Ans: Data integration

24. Data Mining is not a business solution; it is just a technology. (True/False)
Ans: True

25. ___ is a random error or variance in measured variables.
Ans: Noise

26. State true or false
I. BI applications can also help managers to be better informed about actions that a company’s competitors are taking
II. BI can help companies share selected strategic information with business partners.
III. BI 2.0″ is used to describe the acquisition, provision and analysis of “real-time” data
A. i-T, ii-F, iii-F
B. i-T, ii-T, iii-F
C. i-T, ii-F, iii-T
D. i-T, ii-T, iii-T
Ans: D.

27. ___ routines attempt to fill in missing values, smooth out noise while identifying outlines, and correct inconsistencies in the data.
Ans: Data cleaning

28. ___ is used to refer to systems and technologies that provide the business with the means for decision-makers to extract personalized meaningful information about their business and industry.
Ans: Business Intelligence

29. In ___ each value in a bin is replaced by the mean value of the bin.
Ans: Smoothing by bin means

30. ___ regression involves finding the “best” line to fit two variables so that one variable can be used to predict the other.
Ans: Linear

31. ___ works to remove the noise from the data that includes techniques like binning, clustering, and regression.
Ans: Smoothing

32. Redundancies can be detected by correlation analysis. (True/False)
Ans: True

33. The ___ technique uses encoding mechanisms to reduce the data set size.
Ans: Data compression

34. In which Strategy of data reduction redundant attributes are detected.
A. Date cube aggregation
B. Numerosity reduction
C. Data compression
D. Dimension reduction
Ans: D. Dimension reduction

35. ___ hierarchies can be used to reduce the data by collecting and replacing low-level concepts by higher-level concepts.
Ans: Concept

36. The ___ rule can be used to segment numeric data into relatively uniform, “natural” intervals.
Ans: 3-4-5

37. Oracle, SQL/Server, DB2 are examples for ___.
Ans: DBMS

38. Data Base Management System (DBMS) supports query languages. (True/False)
Ans: True

39. The ___ item sets find all sets of items (items sets) whose support is greater than the user-specified minimum support, σ.
Ans: Frequent set

40. A frequent set is a ___ if it is a frequent set and no superset of this is a frequent set.
Ans: Maximal frequent set

41. ___ techniques are used to detect relationships or associations between specific values of categorical variables in large data sets.
Ans: Association rule mining

42. A Decision Tree is a ___ model.
Ans: Predictive model

43. Using a decision tree, only categorical variables would be modelled. (True/False).
Ans: False

44. Clustering is an unsupervised learning method (True/false).
Ans: False

45. Neural networks are made up of many ___.
Ans: Artificial neurons

46. For a given transaction database T, a ___ is an expression of the form X => Y, where X and Y are subsets of A and X => Y holds with confidence Ʈ, if Ʈ% of transactions in D support X also support Y.
Ans: Association rule

47. The ___ rule describes associations between quantitative items or attributes.
Ans: Quantitative association

48. The ___ step eliminates the extensions of (k-1) – itemsets, which are not found to be frequent, from being considered for counting support.
Ans: Pruning

49. In the first phase of the Partition algorithm, the algorithm logically divides the database into a number of ___.
Ans: non – overlapping partitions.

50. The a priori algorithm operates in a ___ and ___.
Ans: bottom-up, breadth-first search method.

51. ___ algorithm works like a train running over the data, with stops at intervals M between transactions. When the train reaches the end of the transaction file it completes one path.
Ans: DIC Algorithm

52. FP–Tree Growth Algorithm can be implemented in ___ Phases.
Ans: Two

53. FP – tree stands for ___.
Ans: Frequent pattern tree

54. Data mining systems should provide capabilities to mine association rules at multiple levels of abstraction and traverse easily among different abstraction spaces (True/False).
Ans: True

55. Which one of the following is alternative search strategies for mining multiple-level associations with reduced support?
a) Level – by level independent
b) Level – cross-filtering by a single item
c) Level – cross-filtering by k – itemset:
d) All the above
Ans: d) All the above

56. Which of the following is NOT a common binning strategy?
a) Equiwidth binning,
b) Equidepth binning,
c) Homogeneity – based binning,
d) Equilength binning
Ans: d) Equilength binning

57. Association rules that involve two or more dimension or predicates can be referred to as ___.
Ans: Multidimensional association rules.

58. An algorithm that performs a series of “walks” through itemset space is called a ___.
Ans: Random walk algorithm.

59. What are knowledge type constraints?
Ans: They specify the type of knowledge to be mined.

60. A standard measure of within-cluster similarity is ___.
Ans: variance

61. The process of grouping a set of physical or abstract objects into classes of similar objects is called ___.
Ans: Cluster

62. Clustering may also be considered as ___.
Ans: Segmentation

63. Clustering is also called:
a. Segmentation
b. Compression
c. Partitions with similar objects
d. All the above
Ans: d. All the above

64. Clustering is used only in data mining (True/False).
Ans: True

65. Clustering is a form of learning by observation rather than ___.
Ans: By example

66. Weight and height of an individual fall into ___ kind of variables.
Ans: Continuous

67. In the K-means algorithm for partitioning, each cluster is represented by the ___ of objects in the cluster.
Ans: Means

68. K-means clustering requires prior knowledge about number clusters required as its input.(True/False).
Ans: True

69. One form of unsupervised learning is ___.
Ans: Clustering

70. ___ software provides a set of partitioned clustering algorithms that treat the clustering problem as an optimization process.
Ans: CLUTO

71. Data classification is a ___ step process.
Ans: Two

72. ___ can be viewed as the construction and use of a model to assess the class of an unlabeled sample, or to assess the value or value ranges of an attribute that a given sample is likely to have.
Ans: Prediction

73. ___ of data removes or reduces noise (by applying smoothing techniques) and the treatment of missing values.
Ans: Pre-processing

74. ___ method refers to the ability to construct the model efficiently given a large amount of data.
Ans: Scalability

75. What is a decision tree?
Ans: This is a flow – chart – like a tree structure, where each internal node denotes a test on an attribute, each branch represents an outcome of the test, and leaf nodes represent classes or class distributions.

76. The basic algorithm for decision tree induction is a ___ algorithm.
Ans: greedy

77. The ___ measure is used to select the test attribute at each node in the tree.
Ans: information gain

78. A user session is a ___ record spanning the entire Web.
Ans: Clickstream record

79. ___ is simple text files that are automatically generated every time someone accesses one Website.
Ans: Log File

80. ___ files are frequently used in sequential mining.
Ans: Web log files

81. ___ is used to examine the structure of a particular website and collate and analyze related data.
Ans: Structural mining

82. Which of the following techniques are concerned about user navigation accessing?
a. Web structural mining
b. Web usage mining
c. Web content mining
d. Web data definition mining
Ans: b. Web usage mining

83. Web data is ___.
a. Structured data
b. Un-structured data
c. Only text data
d. Binary data
Ans: b. Un-structured data

84. ___ Web mining involves the development of Sophisticated Artificial Intelligence systems.
Ans: an agent-based approach

85. The ___ approaches to Web mining have generally focused on techniques for integrating and organizing the heterogeneous and semi-structured data on the Web into more structured and high-level collections of resources.
Ans: database

86. Association rules involving multimedia objects can be mined in ___ and ___ databases.
Ans: Image and video

87. In ___ approach, the signature of an image includes color histograms based on the color composition of an image regardless of its scale or orientation.
Ans: Color histogram-based signature

88. Which of the following are the measures of the text retrieval documents?
a. Precision
b. Recall
c. F-score
d. a,b,c
Ans: d. a,b,c

89. Data stored in most text databases are ___.
Ans: Semi-structured

90. Which of the following is the first step in text retrieval systems?
a. Stemming
b. Term words finding
c. Tokenization
d. Replacing the null data with keywords
Ans: c. Tokenization

91. Which of the following are the stop words?
a. A
b. The
c. of
d. a,b,c
Ans: d. a,b,c

92. Text databases are also called ___.
Ans: Document databases

93. Insurance and direct mail are two industries that rely on ___ to make profitable business decisions.
Ans: data analysis

94. To aid decision-making, analysts construct ___ models using warehouse data to predict the outcomes of a variety of decision alternatives.
Ans: predictive

95. A ___ profile is a model that predicts the future purchasing behaviour of an individual customer, given historical transaction data for both the individual and for the larger population of all of a particular company’s customers.
Ans: predictive

96. Data mining can be used to help predict future patient behaviour and to improve treatment programs (True/False).
Ans: True

98. Data mining in the telecommunication industry helps to understand the business involved, identify telecommunication patterns (True/False).
Ans: True

99. GDP stands for ___.
Ans: gross domestic product

100. ___ is proving to be a critical link between theory, simulation, and experiment.
Ans: data-intensive computing

101. IDS are based on ___ that are developed by the manual encoding of expert knowledge.
Ans: Handcrafted signatures

102. Choose the correct option.
Data mining can be used to improve ___.
a) Efficiency
b) Quality of data
c) Marketing
d) All the above
Ans: D. All the above.

103. To improve accuracy, data mining programs are used to analyze audit data and extract features that can distinguish normal activities from intrusions. (True/False)
Ans: True

104. Data mining-based IDSs (especially anomaly detection systems) have higher false-positive rates than traditional handcrafted signature-based methods. (True/False)
Ans: True

105. ___ is a new class of intrusion detection algorithms that do not rely on labelled data.
Ans: Unsupervised anomaly detection

106. ___ algorithm uses the frequency distribution of each feature’s values to proportionally generate a sufficient amount of anomalies.
Ans: Distribution Based Artificial Anomaly

107. OLAP typically includes the following kinds of analyses: simple, comparison, trend, ___ and ___.
Ans: Variance and ranking

108. Patient Rule Induction Method (PRIM) and Weighted Item Sets (WIS), is a type of ___ technique.
Ans: Association rule

109. ___ tools cannot discover high average regions or find new patterns in data.
Ans: OLAP

110. ___ method is useful for finding patterns or associations between attributes.
Ans: WIS

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Conclusion

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