Q. In K-Means clustering, what does the 'K' represent?
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A.
The number of features
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B.
The number of clusters
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C.
The number of iterations
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D.
The number of data points
Solution
'K' represents the number of clusters that the algorithm will create from the data.
Correct Answer:
B
— The number of clusters
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Q. What is DBSCAN primarily used for in clustering?
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A.
To find spherical clusters
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B.
To identify noise and outliers
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C.
To classify data points
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D.
To reduce dimensionality
Solution
DBSCAN is effective for identifying clusters of varying shapes and for detecting noise and outliers.
Correct Answer:
B
— To identify noise and outliers
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Q. What is the main difference between K-Means and DBSCAN clustering algorithms?
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A.
K-Means is faster than DBSCAN
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B.
DBSCAN can find clusters of arbitrary shape
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C.
K-Means requires labeled data
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D.
DBSCAN is only for high-dimensional data
Solution
DBSCAN can find clusters of arbitrary shape, while K-Means assumes spherical clusters.
Correct Answer:
B
— DBSCAN can find clusters of arbitrary shape
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Q. What is the main limitation of K-Means clustering?
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A.
It is computationally expensive
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B.
It requires a predefined number of clusters
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C.
It can only handle numerical data
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D.
It is sensitive to outliers
Solution
K-Means requires the user to specify the number of clusters in advance, which can be a limitation.
Correct Answer:
B
— It requires a predefined number of clusters
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Q. What is the primary goal of clustering in unsupervised learning?
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A.
To predict future outcomes
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B.
To group similar data points together
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C.
To label data points
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D.
To reduce dimensionality
Solution
Clustering aims to group similar data points together based on their features without prior labels.
Correct Answer:
B
— To group similar data points together
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Q. What type of data is best suited for clustering?
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A.
Labeled data
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B.
Time series data
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C.
Unlabeled data
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D.
Sequential data
Solution
Clustering is an unsupervised learning technique, making it best suited for unlabeled data.
Correct Answer:
C
— Unlabeled data
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Q. Which evaluation metric is most suitable for assessing clustering performance?
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A.
Accuracy
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B.
F1 Score
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C.
Adjusted Rand Index
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D.
Mean Absolute Error
Solution
The Adjusted Rand Index measures the similarity between two data clusterings, making it suitable for evaluating clustering performance.
Correct Answer:
C
— Adjusted Rand Index
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Q. Which of the following algorithms is commonly used for clustering?
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A.
Linear Regression
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B.
K-Means
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C.
Support Vector Machine
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D.
Decision Tree
Solution
K-Means is a popular clustering algorithm that partitions data into K distinct clusters.
Correct Answer:
B
— K-Means
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Q. Which of the following applications can benefit from clustering?
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A.
Customer segmentation
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B.
Spam detection
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C.
Image classification
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D.
Time series forecasting
Solution
Customer segmentation is a common application of clustering, where customers are grouped based on purchasing behavior.
Correct Answer:
A
— Customer segmentation
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Q. Which of the following is a real-world application of clustering?
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A.
Spam detection in emails
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B.
Image classification
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C.
Market segmentation
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D.
Sentiment analysis
Solution
Market segmentation is a common application of clustering, where customers are grouped based on purchasing behavior.
Correct Answer:
C
— Market segmentation
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Q. Which of the following is NOT a characteristic of hierarchical clustering?
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A.
Creates a tree-like structure
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B.
Can be agglomerative or divisive
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C.
Requires the number of clusters to be specified in advance
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D.
Can visualize data relationships
Solution
Hierarchical clustering does not require the number of clusters to be specified in advance; it builds a hierarchy of clusters.
Correct Answer:
C
— Requires the number of clusters to be specified in advance
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