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Which clustering method is particularly effective for large datasets?
Which clustering method is particularly effective for large datasets?
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Q1
Which clustering method is particularly effective for large datasets?
Hierarchical clustering
K-means clustering
DBSCAN
Gaussian Mixture Models
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K-means clustering is particularly effective for large datasets due to its efficiency and scalability.
Questions & Step-by-step Solutions
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Q: Which clustering method is particularly effective for large datasets?
Solution:
K-means clustering is particularly effective for large datasets due to its efficiency and scalability.
Steps: 5
Show Steps
Step 1: Understand what clustering means. Clustering is a way to group similar items together.
Step 2: Learn about different clustering methods. There are many methods, but we will focus on K-means.
Step 3: Know what K-means clustering is. K-means is a method that divides data into 'K' groups based on their features.
Step 4: Recognize why K-means is good for large datasets. It is fast and can handle a lot of data points efficiently.
Step 5: Understand scalability. K-means can easily work with more data without slowing down too much.
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