What is a key characteristic of DBSCAN compared to K-means?
Correct Answer: DBSCAN can identify clusters of arbitrary shape and does not require the number of clusters to be specified in advance.
- Step 1: Understand that DBSCAN and K-means are both clustering algorithms used to group data points.
- Step 2: Note that K-means requires you to specify the number of clusters (K) before running the algorithm.
- Step 3: Recognize that DBSCAN does not need you to specify the number of clusters in advance.
- Step 4: Learn that DBSCAN can find clusters that are not just round or spherical, meaning it can identify clusters of any shape.
- Step 5: Compare this to K-means, which tends to create clusters that are more circular and evenly sized.
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