Here are 25 interview questions along with their answers related to K-means clustering
1. What is K-means clustering? Answer: K-means clustering is an unsupervised machine learning algorithm used to partition a dataset into K clusters based on similarity. 2. How does K-means clustering work? Answer: K-means clustering works by iteratively assigning data points to the nearest cluster centroid and updating the centroids based on the mean of the data points assigned to each cluster. 3. What is the objective function in K-means clustering? Answer: The objective function in K-means clustering is to minimize the sum of squared distances between each data point and its assigned cluster centroid. 4. What are the key parameters in K-means clustering? Answer: The key parameters in K-means clustering are the number of clusters (K) and the initial cluster centroids. 5. How do you choose the number of clusters (K) in K-means clustering? Answer: The number of clusters (K) in K-means clustering is often chosen based on domain knowledge, the Elbow method, or...