Which clustering algorithm is best for identifying clusters of varying shapes an
Practice Questions
Q1
Which clustering algorithm is best for identifying clusters of varying shapes and sizes?
K-Means
DBSCAN
Agglomerative Clustering
Gaussian Mixture Model
Questions & Step-by-Step Solutions
Which clustering algorithm is best for identifying clusters of varying shapes and sizes?
Step 1: Understand what clustering means. Clustering is a way to group similar items together.
Step 2: Learn about different clustering algorithms. There are many algorithms, but they work differently.
Step 3: Identify the problem with some algorithms. Some clustering algorithms assume that clusters are round and of similar size.
Step 4: Discover DBSCAN. DBSCAN stands for Density-Based Spatial Clustering of Applications with Noise.
Step 5: Understand how DBSCAN works. It groups points that are close together based on density, meaning it can find clusters of different shapes and sizes.
Step 6: Conclude that DBSCAN is a good choice for identifying clusters that are not uniform.
