Data Mining Questions and Answers | DM | MCQ

In this Data Mining MCQ , we will cover these topics such as data mining, techniques for data mining, techniques data mining, what is data mining, define data mining, definition data mining, data mining and analysis, process of data mining, data analysis and mining, data mining techniques, software data mine, data mining processes, data mining in research, concept of data mining and so on.

Question 1
This  clustering algorithm terminates when mean values computed for the current iteration of the algorithm are identical to the computed mean values for the previous iteration
Select one:
a. K-Means clustering
b. conceptual clustering
c. expectation maximization
d. agglomerative clustering
[bg_collapse view=”button-green” color=”#FFF” icon=”arrow” expand_text=”Show Answer” collapse_text=”Hide Answer” ] Feedback: K-Means clustering
[/bg_collapse]
Question 2
This clustering approach initially assumes that each data instance represents a single cluster.
Select one:
a. expectation maximization
b. K-Means clustering
c. agglomerative clustering
d. conceptual clustering
[bg_collapse view=”button-green” color=”#FFF” icon=”arrow” expand_text=”Show Answer” collapse_text=”Hide Answer” ] Feedback:
[/bg_collapse]
Question 3
The correlation coefficient for two real-valued attributes is –0.85. What does this value tell you?
Select one:
a. The attributes are not linearly related.
b. As the value of one attribute decreases the value of the second attribute increases.
c. As the value of one attribute increases the value of the second attribute also increases.
d. The attributes show a linear relationship
[bg_collapse view=”button-green” color=”#FFF” icon=”arrow” expand_text=”Show Answer” collapse_text=”Hide Answer” ] Feedback: As the value of one attribute decreases the value of the second attribute increases.
[/bg_collapse]
Question 4
Time Complexity of k-means is given by
Select one:
a. O(mn)
b. O(tkn)
c. O(kn)
d. O(t2kn)
[bg_collapse view=”button-green” color=”#FFF” icon=”arrow” expand_text=”Show Answer” collapse_text=”Hide Answer” ] Feedback: O(tkn)
[/bg_collapse]
Question 5
Given a rule of the form IF X THEN Y, rule confidence is defined as the conditional probability that
Select one:
a. Y is false when X is known to be false.
b. Y is true when X is known to be true.
c. X is true when Y is known to be true
d. X is false when Y is known to be false.
[bg_collapse view=”button-green” color=”#FFF” icon=”arrow” expand_text=”Show Answer” collapse_text=”Hide Answer” ] Feedback: Y is true when X is known to be true.

[/bg_collapse]
Question 6
Chameleon is
Select one:
a. Density based clustering algorithm
b. Partitioning based algorithm
c. Model based algorithm
d. Hierarchical clustering algorithm
[bg_collapse view=”button-green” color=”#FFF” icon=”arrow” expand_text=”Show Answer” collapse_text=”Hide Answer” ] Feedback: Hierarchical clustering algorithm
[/bg_collapse]
Question 7
In _________ clusterings, points may belong to multiple clusters
Select one:
a. Non exclusivce
b. Partial
c. Fuzzy
d. Exclusive
[bg_collapse view=”button-green” color=”#FFF” icon=”arrow” expand_text=”Show Answer” collapse_text=”Hide Answer” ] Feedback: Fuzzy
[/bg_collapse]
Question 8
Find odd man out
Select one:
a. DBSCAN
b. K mean
c. PAM
d. K medoid
[bg_collapse view=”button-green” color=”#FFF” icon=”arrow” expand_text=”Show Answer” collapse_text=”Hide Answer” ] Feedback: DBSCAN
[/bg_collapse]
Question 9
Which statement is true about the K-Means algorithm?
Select one:
a. The output attribute must be cateogrical.
b. All attribute values must be categorical.
c. All attributes must be numeric
d. Attribute values may be either categorical or numeric
[bg_collapse view=”button-green” color=”#FFF” icon=”arrow” expand_text=”Show Answer” collapse_text=”Hide Answer” ] Feedback: All attributes must be numeric
[/bg_collapse]



Question 10
This data transformation technique works well when minimum and maximum values for a real-valued attribute are known.
Select one:
a. z-score normalization
b. min-max normalization
c. logarithmic normalization
d. decimal scaling
[bg_collapse view=”button-green” color=”#FFF” icon=”arrow” expand_text=”Show Answer” collapse_text=”Hide Answer” ] Feedback: min-max normalization
[/bg_collapse]
Question 11
The number of iterations in apriori ___________
Select one:
a. increases with the size of the data
b. decreases with the increase in size of the data
c. increases with the size of the maximum frequent set
d. decreases with increase in size of the maximum frequent set
[bg_collapse view=”button-green” color=”#FFF” icon=”arrow” expand_text=”Show Answer” collapse_text=”Hide Answer” ] Feedback: increases with the size of the maximum frequent set
[/bg_collapse]
Question 12
Which of the following are interestingness measures for association rules?
Select one:
a. recall
b. lift
c. accuracy
d. compactness
[bg_collapse view=”button-green” color=”#FFF” icon=”arrow” expand_text=”Show Answer” collapse_text=”Hide Answer” ] Feedback: lift
[/bg_collapse]
Question 13
Which one of the following is not a major strength of the neural network approach?
Select one:
a. Neural network learning algorithms are guaranteed to converge to an optimal solution
b. Neural networks work well with datasets containing noisy data.
c. Neural networks can be used for both supervised learning and unsupervised clustering
d. Neural networks can be used for applications that require a time element to be included in the data
[bg_collapse view=”button-green” color=”#FFF” icon=”arrow” expand_text=”Show Answer” collapse_text=”Hide Answer” ] Feedback: Neural network learning algorithms are guaranteed to converge to an optimal solution
[/bg_collapse]
Question 14
Find odd man out
Select one:
a. K medoid
b. K mean
c. DBSCAN
d. PAM
[bg_collapse view=”button-green” color=”#FFF” icon=”arrow” expand_text=”Show Answer” collapse_text=”Hide Answer” ] Feedback: DBSCAN
[/bg_collapse]
Question 15
Given a frequent itemset L, If |L| = k, then there are
Select one:
a. 2k   – 1 candidate association rules
b. 2k   candidate association rules
c. 2k   – 2 candidate association rules
d. 2k -2 candidate association rules
[bg_collapse view=”button-green” color=”#FFF” icon=”arrow” expand_text=”Show Answer” collapse_text=”Hide Answer” ] Feedback: 2k -2 candidate association rules
[/bg_collapse]
Question 16
. _________ is an example for case based-learning
Select one:
a. Decision trees
b. Neural networks
c. Genetic algorithm
d. K-nearest neighbor
[bg_collapse view=”button-green” color=”#FFF” icon=”arrow” expand_text=”Show Answer” collapse_text=”Hide Answer” ] Feedback: K-nearest neighbor
[/bg_collapse]
Question 17
The average positive difference between computed and desired outcome values.
Select one:
a. mean positive error
b. mean squared error
c. mean absolute error
d. root mean squared error
[bg_collapse view=”button-green” color=”#FFF” icon=”arrow” expand_text=”Show Answer” collapse_text=”Hide Answer” ] Feedback: mean absolute error
[/bg_collapse]
Question 18
Frequent item sets is
Select one:
a. Superset of only closed frequent item sets
b. Superset of only maximal frequent item sets
c. Subset of maximal frequent item sets
d. Superset of both closed frequent item sets and maximal frequent item sets
[bg_collapse view=”button-green” color=”#FFF” icon=”arrow” expand_text=”Show Answer” collapse_text=”Hide Answer” ] Feedback: Superset of both closed frequent item sets and maximal frequent item sets
[/bg_collapse]
Question 19
1. Assume that we have a dataset containing information about 200 individuals.  A supervised data mining session has discovered the following rule:
IF  age < 30 & credit card insurance = yes   THEN life insurance = yes
Rule Accuracy:    70%   and  Rule Coverage:   63%
How many individuals in the class life insurance= no have credit card insurance and are less than 30 years old?
Select one:
a. 63
b. 30
c. 38
d. 70
[bg_collapse view=”button-green” color=”#FFF” icon=”arrow” expand_text=”Show Answer” collapse_text=”Hide Answer” ] Feedback: 38
[/bg_collapse]



Question 20
Use the three-class confusion matrix below to answer percent of the instances were correctly classified?

[/bg_collapse]
Question 21
Which of the following is cluster analysis?
Select one:
a. Simple segmentation
b. Grouping similar objects
c. Labeled classification
d. Query results grouping
[bg_collapse view=”button-green” color=”#FFF” icon=”arrow” expand_text=”Show Answer” collapse_text=”Hide Answer” ] Feedback: Grouping similar objects
[/bg_collapse]
Question 22
A good clustering method will produce high quality clusters with
Select one:
a. high inter class similarity
b. low intra class similarity
c. high intra class similarity
d. no inter class similarity
[bg_collapse view=”button-green” color=”#FFF” icon=”arrow” expand_text=”Show Answer” collapse_text=”Hide Answer” ] Feedback: high intra class similarity
[/bg_collapse]
Question 23
Which two parameters are needed for DBSCAN
Select one:
a. Min threshold
b. Min points and eps
c. Min sup and min confidence
d. Number of centroids
[bg_collapse view=”button-green” color=”#FFF” icon=”arrow” expand_text=”Show Answer” collapse_text=”Hide Answer” ] Feedback: Min points and eps
[/bg_collapse]
Question 24
Which statement is true about neural network and linear regression models?
Select one:
a. Both techniques build models whose output  is determined by a  linear sum of weighted input attribute values.
b. The output of both models is a categorical attribute value.
c. Both models require numeric attributes to range between 0 and 1.
d. Both models require input attributes to be numeric.
[bg_collapse view=”button-green” color=”#FFF” icon=”arrow” expand_text=”Show Answer” collapse_text=”Hide Answer” ] Feedback: Both models require input attributes to be numeric.
[/bg_collapse]
Question 25
In Apriori algorithm, if 1 item-sets are 100, then the number of candidate 2 item-sets are
Select one:
a. 100
b. 4950
c. 200
d. 5000
[bg_collapse view=”button-green” color=”#FFF” icon=”arrow” expand_text=”Show Answer” collapse_text=”Hide Answer” ] Feedback: 4950
[/bg_collapse]
Question 26
Significant Bottleneck in the Apriori algorithm is
Select one:
a. Finding frequent itemsets
b. Pruning
c. Candidate generation
d. Number of iterations
[bg_collapse view=”button-green” color=”#FFF” icon=”arrow” expand_text=”Show Answer” collapse_text=”Hide Answer” ] Feedback: Candidate generation
[/bg_collapse]
Question 27
The concept of core, border and noise points fall into this category?
Select one:
a. DENCLUE
b. Subspace clustering
c. Grid based
d. DBSCAN
[bg_collapse view=”button-green” color=”#FFF” icon=”arrow” expand_text=”Show Answer” collapse_text=”Hide Answer” ] Feedback: DBSCAN
[/bg_collapse]
Question 28
The correlation coefficient for two real-valued attributes is –0.85. What does this value tell you?
Select one:
a. The attributes show a linear relationship
b. The attributes are not linearly related.
c. As the value of one attribute increases the value of the second attribute also increases.
d. As the value of one attribute decreases the value of the second attribute increases.
[bg_collapse view=”button-green” color=”#FFF” icon=”arrow” expand_text=”Show Answer” collapse_text=”Hide Answer” ] Feedback: As the value of one attribute decreases the value of the second attribute increases.
[/bg_collapse]
Question 29
Machine learning techniques differ from statistical techniques in that machine learning methods
Select one:
a. are better able to deal with missing and noisy data
b. typically assume an underlying distribution for the data
c. have trouble with large-sized datasets
d. are not able to explain their behavior.
[bg_collapse view=”button-green” color=”#FFF” icon=”arrow” expand_text=”Show Answer” collapse_text=”Hide Answer” ] Feedback: are better able to deal with missing and noisy data
[/bg_collapse]



Question 30
The probability of a hypothesis before the presentation of evidence.
Select one:
a. a priori
b. posterior
c. conditional
d. subjective
[bg_collapse view=”button-green” color=”#FFF” icon=”arrow” expand_text=”Show Answer” collapse_text=”Hide Answer” ] Feedback: a priori
[/bg_collapse]
Question 31
KDD represents extraction of
Select one:
a. data
b. knowledge
c. rules
d. model
[bg_collapse view=”button-green” color=”#FFF” icon=”arrow” expand_text=”Show Answer” collapse_text=”Hide Answer” ] Feedback: knowledge
[/bg_collapse]
Question 32
Which statement about outliers is true?
Select one:
a. Outliers should be part of the training dataset but should not be present in the test data.
b. Outliers should be identified and removed from a dataset.
c. The nature of the problem determines how outliers are used
d. Outliers should be part of the test dataset but should not be present in the training data.
[bg_collapse view=”button-green” color=”#FFF” icon=”arrow” expand_text=”Show Answer” collapse_text=”Hide Answer” ] Feedback: The nature of the problem determines how outliers are used
[/bg_collapse]
Question 33
The most general form of distance is
Select one:
a. Manhattan
b. Eucledian
c. Mean
d. Minkowski
[bg_collapse view=”button-green” color=”#FFF” icon=”arrow” expand_text=”Show Answer” collapse_text=”Hide Answer” ] Feedback: Minkowski
[/bg_collapse]
Question 34
Arbitrary shaped clusters can be found by using
Select one:
a. Density methods
b. Partitional methods
c. Hierarchical methods
d. Agglomerative
[bg_collapse view=”button-green” color=”#FFF” icon=”arrow” expand_text=”Show Answer” collapse_text=”Hide Answer” ] Feedback: Density methods
[/bg_collapse]
Question 35
Which Association Rule would you prefer
Select one:
a. High support and medium confidence
b. High support and low confidence
c. Low support and high confidence
d. Low support and low confidence
[bg_collapse view=”button-green” color=”#FFF” icon=”arrow” expand_text=”Show Answer” collapse_text=”Hide Answer” ] Feedback: Low support and high confidence
[/bg_collapse]
Question 36
With Bayes theorem the probability of hypothesis H¾ specified by P(H) ¾ is referred to as
Select one:
a. a conditional probability
b. an a priori probability
c. a bidirectional probability
d. a posterior probability
[bg_collapse view=”button-green” color=”#FFF” icon=”arrow” expand_text=”Show Answer” collapse_text=”Hide Answer” ] Feedback: an a priori probability
[/bg_collapse]
Question 37
In a Rule based classifier, If there is a rule for each combination of attribute values, what do you called that rule set R
Select one:
a. Exhaustive
b. Inclusive
c. Comprehensive
d. Mutually exclusive
[bg_collapse view=”button-green” color=”#FFF” icon=”arrow” expand_text=”Show Answer” collapse_text=”Hide Answer” ] Feedback: Exhaustive
[/bg_collapse]
Question 38
The apriori property means
Select one:
a. If a set cannot pass a test, its supersets will also fail the same test
b. To decrease the efficiency, do level-wise generation of frequent item sets
c. To improve the efficiency, do level-wise generation of frequent item sets
d. If a set can pass a test, its supersets will fail the same test
[bg_collapse view=”button-green” color=”#FFF” icon=”arrow” expand_text=”Show Answer” collapse_text=”Hide Answer” ] Feedback: If a set cannot pass a test, its supersets will also fail the same test
[/bg_collapse]
Question 39
If  an item set ‘XYZ’ is a frequent item set, then all subsets of that frequent item set are

Select one:
a. Undefined
b. Not frequent
c. Frequent
d. Can not say
[bg_collapse view=”button-green” color=”#FFF” icon=”arrow” expand_text=”Show Answer” collapse_text=”Hide Answer” ] Feedback: Frequent
[/bg_collapse]



Question 40
Clustering is ___________ and is example of ____________learning
Select one:
a. Predictive and supervised
b. Predictive and unsupervised
c. Descriptive and supervised
d. Descriptive and unsupervised
[bg_collapse view=”button-green” color=”#FFF” icon=”arrow” expand_text=”Show Answer” collapse_text=”Hide Answer” ] Feedback: Descriptive and unsupervised
[/bg_collapse]
Question 41
The probability that a person owns a sports car given that they subscribe to automotive magazine is 40%.  We also know that 3% of the adult population subscribes to automotive magazine. The probability of a person owning a sports car given that they don’t subscribe to automotive magazine is 30%.  Use this information to compute the probability that a person subscribes to automotive magazine given that they own a sports car
Select one:
a. 0.0368
b. 0.0396
c. 0.0389
d. 0.0398
[bg_collapse view=”button-green” color=”#FFF” icon=”arrow” expand_text=”Show Answer” collapse_text=”Hide Answer” ] Feedback: 0.0396
[/bg_collapse]
Question 42
Simple regression assumes a __________ relationship between the input  attribute and output attribute.
Select one:
a. quadratic
b. inverse
c. linear
d. reciprocal
[bg_collapse view=”button-green” color=”#FFF” icon=”arrow” expand_text=”Show Answer” collapse_text=”Hide Answer” ] Feedback: linear
[/bg_collapse]
Question 43
Which of the following algorithm comes under the classification
Select one:
a. Apriori
b. Brute force
c. DBSCAN
d. K-nearest neighbor
[bg_collapse view=”button-green” color=”#FFF” icon=”arrow” expand_text=”Show Answer” collapse_text=”Hide Answer” ] Feedback: K-nearest neighbor
[/bg_collapse]
Question 44
Hierarchical agglomerative clustering is typically visualized as?
Select one:
a. Dendrogram
b. Binary trees
c. Block diagram
d. Graph
[bg_collapse view=”button-green” color=”#FFF” icon=”arrow” expand_text=”Show Answer” collapse_text=”Hide Answer” ] Feedback: Dendrogram
[/bg_collapse]
Question 45
The _______ step eliminates the extensions of (k-1)-itemsets which are not found to be frequent,from being considered for counting support
Select one:
a. Partitioning
b. Candidate generation
c. Itemset eliminations
d. Pruning
[bg_collapse view=”button-green” color=”#FFF” icon=”arrow” expand_text=”Show Answer” collapse_text=”Hide Answer” ] Feedback: Pruning
[/bg_collapse]
Question 46
To determine association rules from frequent item sets
Select one:
a. Only minimum confidence needed
b. Neither support not confidence needed
c. Both minimum support and confidence are needed
d. Minimum support is needed
[bg_collapse view=”button-green” color=”#FFF” icon=”arrow” expand_text=”Show Answer” collapse_text=”Hide Answer” ] Feedback: Both minimum support and confidence are needed
[/bg_collapse]
Question 47
What is the final resultant cluster size in Divisive algorithm, which is one of the hierarchical clustering approaches?
Select one:
a. Zero
b. Three
c. singleton
d. Two
[bg_collapse view=”button-green” color=”#FFF” icon=”arrow” expand_text=”Show Answer” collapse_text=”Hide Answer” ] Feedback: singleton
[/bg_collapse]
Question 48
If {A,B,C,D} is a frequent itemset, candidate rules which is not possible is
Select one:
a. C –> A
b. D –>ABCD
c. A –> BC
d. B –> ADC
[bg_collapse view=”button-green” color=”#FFF” icon=”arrow” expand_text=”Show Answer” collapse_text=”Hide Answer” ] Feedback: D –>ABCD
[/bg_collapse]
Question 49
Which Association Rule would you prefer
Select one:
a. High support and low confidence
b. Low support and high confidence
c. Low support and low confidence
d. High support and medium confidence
[bg_collapse view=”button-green” color=”#FFF” icon=”arrow” expand_text=”Show Answer” collapse_text=”Hide Answer” ] Feedback: Low support and high confidence

Question 55
What does K refers in the K-Means algorithm which is a non-hierarchical clustering approach?
Select one:
a. Complexity
b. Fixed value
c. No of iterations
d. number of clusters
[bg_collapse view=”button-green” color=”#FFF” icon=”arrow” expand_text=”Show Answer” collapse_text=”Hide Answer” ] Feedback: number of clusters[/bg_collapse]