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

The k-means clustering algorithm works by (Select one) A. iteratively improving the position of k centroids in the sample space until an optimal placement is found. B. starting with one point in the sample space, finding more points in the space within a neighborhood ℇ until no more points can be found, and then repeating this process for k-1 points. C. iteratively determining the Gaussian distribution (via its mean and standard deviation) of k clusters until the probabilities of all points in the sample space are maximized. D. pairing each point with another point such that their distance is minimized, and then repeating this process with larger groups of points until there are only k clusters remaining.

How is the final set of clusters determined in the k-means algorithm?Select one:a.By selecting the set of clusters that minimize the sum of squared errorsb.By selecting the set of clusters that maximize the within-cluster variancec.By selecting the set of clusters that maximize the sum of squared errorsd.By selecting the set of clusters that minimize the within-cluster variance

What is the main goal of the k-means algorithm?Select one:a.To discover patterns or relationships within a datasetb.To partition a dataset into a specified number of clustersc.To classify data into predefined categoriesd.To predict the value of a continuous target variable

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

How does the k-means algorithm determine which data points belong to which cluster?Select one:a.By evaluating the variance of each clusterb.By evaluating the probability that a data point belongs to each clusterc.By comparing the data point to the characteristics of each clusterd.By computing the distance between data points and the centroid of each cluster