What are the best unsupervised learning algorithms?
Some of the best unsupervised learning algorithms include K-means clustering, hierarchical clustering, and DBSCAN.
Can you explain how K-means clustering works?
K-means clustering partitions data into K distinct clusters based on feature similarity, minimizing variance within each cluster.
What are the advantages of using DBSCAN?
DBSCAN can find arbitrarily shaped clusters and is robust to noise, making it great for real-world data.
Are there any limitations to these algorithms?
Yes, K-means requires specifying the number of clusters in advance, and DBSCAN can struggle with varying densities.
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