What is a disadvantage of K-Means clustering?Answer areaIt is easy to implementIt always converges to the global optimumIt is sensitive to the initial placement of centroidsIt works well with all shapes of clusters

What is an advantage of hierarchical clustering over K-Means?Answer areaIt is less computationally expensiveIt does not require specifying the number of clusters in advanceIt always finds the global optimumIt works better with large datasets

What is the main objective of the K-Means algorithm?Answer areaTo minimize the sum of squared distances between points and their respective cluster centroidsTo maximize the distance between different clustersTo minimize the number of clustersTo maximize the variance within each cluster

Which of the following is a disadvantage of clustering?Answer areaIt can handle only numerical dataThe results can be highly sensitive to the choice of distance metricIt requires a lot of labeled training dataIt is always computationally expensive

Which of the following is true about K-Means clustering?Answer areaIt is a density-based clustering methodIt assigns each point to the nearest cluster centerIt creates a hierarchy of clustersIt can handle clusters of varying density

Question 1The objective of k-means clustering is:1 pointSeparate dissimilar samples and group similar onesMinimize the cost function via gradient descentYield the highest out of sample accuracyMaximize the number of correctly classified data points