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

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

There are several clustering methods, 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 intensive. DBSCAN is great for identifying clusters of varying shapes and sizes but requires setting parameters.

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 and disadvantages of hierarchical clustering?

Hierarchical clustering's main advantage is that it doesn't require specifying the number of clusters in advance. It also provides a dendrogram, which helps visualize the clustering process. However, it can be computationally expensive and sensitive to noise and outliers.

How does DBSCAN handle noise in the data?

DBSCAN identifies noise as points that do not belong to any cluster. It does this by requiring a minimum number of points within a specified radius to form a cluster. Points that don't meet this criterion are labeled as noise, which helps in separating outliers from meaningful clusters.