What are the differences between K-means and other clustering algorithms?

What are the differences between K-means and other clustering algorithms?

K-means is a popular clustering algorithm that partitions data into K distinct clusters. Unlike hierarchical clustering, which builds a tree of clusters, K-means uses a centroid-based approach. Other algorithms like DBSCAN and Gaussian Mixture Models have different methodologies and are better suited for specific types of data.

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 that, it recalculates the centroids based on the assigned points and repeats the process until the centroids stabilize.

What are the advantages of using K-means?

K-means is efficient and easy to implement, especially for large datasets. It also scales well and can handle large numbers of variables.

Are there any disadvantages to K-means?

Yes, K-means can be sensitive to the initial placement of centroids and may converge to local minima. It also assumes clusters are spherical and equally sized, which may not always be the case.