Q. How can clustering be applied in healthcare?
A.
Grouping patients with similar symptoms
B.
Predicting disease outbreaks
C.
Classifying medical images
D.
Forecasting patient admissions
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Solution
Clustering can be applied in healthcare to group patients with similar symptoms, aiding in diagnosis and treatment planning.
Correct Answer:
A
— Grouping patients with similar symptoms
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Q. How can clustering be used in healthcare?
A.
To predict patient outcomes
B.
To group patients with similar symptoms
C.
To classify diseases
D.
To automate billing processes
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Solution
Clustering can be used in healthcare to group patients with similar symptoms, aiding in diagnosis and treatment planning.
Correct Answer:
B
— To group patients with similar symptoms
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Q. In which scenario would clustering be most beneficial?
A.
Identifying customer groups in a retail dataset
B.
Predicting future sales
C.
Classifying emails as spam or not spam
D.
Forecasting weather patterns
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Solution
Clustering is beneficial for identifying customer groups in a retail dataset, as it helps in understanding different customer profiles.
Correct Answer:
A
— Identifying customer groups in a retail dataset
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Q. In which scenario would clustering be most useful?
A.
Identifying customer groups in a dataset
B.
Predicting future sales
C.
Classifying emails as spam or not
D.
Forecasting weather patterns
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Solution
Clustering is most useful for identifying customer groups in a dataset, as it helps to find natural groupings in the data.
Correct Answer:
A
— Identifying customer groups in a dataset
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Q. What is a key advantage of using clustering in data analysis?
A.
It requires labeled data
B.
It can reveal hidden patterns
C.
It is always more accurate than supervised learning
D.
It eliminates the need for data preprocessing
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Solution
A key advantage of clustering is that it can reveal hidden patterns in data without requiring labeled data.
Correct Answer:
B
— It can reveal hidden patterns
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Q. What is a key benefit of using clustering in social network analysis?
A.
Finding communities within the network
B.
Predicting user behavior
C.
Classifying posts as positive or negative
D.
Identifying outliers in data
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Solution
Clustering helps in finding communities within social networks, allowing for better understanding of user interactions.
Correct Answer:
A
— Finding communities within the network
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Q. What is a potential drawback of using K-means clustering?
A.
It can handle non-spherical clusters
B.
It requires the number of clusters to be specified in advance
C.
It is computationally expensive
D.
It can only be used with numerical data
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Solution
A potential drawback of K-means clustering is that it requires the number of clusters to be specified in advance, which can be challenging.
Correct Answer:
B
— It requires the number of clusters to be specified in advance
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Q. What is the primary goal of clustering in data analysis?
A.
To find natural groupings in data
B.
To predict future outcomes
C.
To classify data into predefined categories
D.
To reduce dimensionality
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Solution
The primary goal of clustering is to find natural groupings in data without prior labels.
Correct Answer:
A
— To find natural groupings in data
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Q. What is the primary goal of clustering in data mining?
A.
To predict future values
B.
To group similar data points
C.
To classify data into predefined categories
D.
To reduce dimensionality
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Solution
The primary goal of clustering in data mining is to group similar data points together based on their features.
Correct Answer:
B
— To group similar data points
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Q. What type of data is clustering most effective with?
A.
Unlabeled data
B.
Labeled data
C.
Time series data
D.
Sequential data
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Solution
Clustering is most effective with unlabeled data, as it seeks to find patterns without predefined categories.
Correct Answer:
A
— Unlabeled data
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Q. Which clustering algorithm is commonly used for grouping similar documents?
A.
K-means
B.
Linear Regression
C.
Decision Trees
D.
Support Vector Machines
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Solution
K-means is a popular clustering algorithm used to group similar documents based on their content.
Correct Answer:
A
— K-means
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Q. Which clustering method is particularly effective for large datasets?
A.
Hierarchical clustering
B.
K-means clustering
C.
DBSCAN
D.
Gaussian Mixture Models
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Solution
K-means clustering is particularly effective for large datasets due to its efficiency and scalability.
Correct Answer:
B
— K-means clustering
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Q. Which of the following is NOT a common use case for clustering?
A.
Market segmentation
B.
Anomaly detection
C.
Image classification
D.
Social network analysis
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Solution
Image classification is typically a supervised learning task, while clustering is used for market segmentation, anomaly detection, and social network analysis.
Correct Answer:
C
— Image classification
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