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Which clustering algorithm is particularly effective for large datasets with noi
Which clustering algorithm is particularly effective for large datasets with noise?
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Practice Questions
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Q1
Which clustering algorithm is particularly effective for large datasets with noise?
Hierarchical clustering
DBSCAN
K-Means
Gaussian Mixture Models
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DBSCAN is effective for large datasets and can identify clusters of varying shapes while handling noise.
Questions & Step-by-step Solutions
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Q: Which clustering algorithm is particularly effective for large datasets with noise?
Solution:
DBSCAN is effective for large datasets and can identify clusters of varying shapes while handling noise.
Steps: 5
Show Steps
Step 1: Understand what clustering algorithms do. They group similar data points together.
Step 2: Learn about DBSCAN, which stands for Density-Based Spatial Clustering of Applications with Noise.
Step 3: Recognize that DBSCAN is good for large datasets because it can handle many data points efficiently.
Step 4: Note that DBSCAN can find clusters of different shapes, not just round ones.
Step 5: Understand that DBSCAN can identify noise, which means it can separate out data points that don't fit into any cluster.
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