What is the main advantage of using DBSCAN over K-Means?
Practice Questions
Q1
What is the main advantage of using DBSCAN over K-Means?
It is faster for large datasets
It can find clusters of arbitrary shape
It requires fewer parameters
It is easier to implement
Questions & Step-by-Step Solutions
What is the main advantage of using DBSCAN over K-Means?
Step 1: Understand that K-Means is a clustering algorithm that groups data into a fixed number of clusters (K).
Step 2: Recognize that K-Means assumes clusters are spherical and evenly sized.
Step 3: Learn that DBSCAN is another clustering algorithm that can find clusters of any shape, not just spherical.
Step 4: Realize that DBSCAN groups points based on their density, meaning it can identify clusters that are close together, even if they are irregularly shaped.
Step 5: Conclude that the main advantage of DBSCAN over K-Means is its ability to find clusters of arbitrary shape, making it more flexible for different types of data.
