What are the different clustering techniques and how do they compare?

What are the different clustering techniques and how do they compare?

There are several clustering techniques, including K-means, hierarchical clustering, and DBSCAN. K-means is efficient for large datasets but requires specifying the number of clusters. Hierarchical clustering builds a tree of clusters but can be computationally expensive. DBSCAN is great for identifying clusters of varying shapes and sizes but requires setting parameters carefully.

Can you explain how K-means works?

Sure! K-means works by initializing 'k' centroids randomly, then assigning each data point to the nearest centroid. After all points are assigned, the centroids are recalculated as the mean of the assigned points. This process repeats until the centroids no longer change significantly.

What are the advantages of using DBSCAN over K-means?

DBSCAN has several advantages: it can find clusters of arbitrary shapes, it doesn't require specifying the number of clusters in advance, and it can effectively handle noise and outliers in the data.

Are there any limitations to hierarchical clustering?

Yes, hierarchical clustering can be computationally intensive, especially with large datasets. It also doesn't scale well and can be sensitive to noise and outliers, which may affect the dendrogram structure.