The endpoint of a k-means clustering algorithm occurs whenGroup of answer choices

In K-Means clustering, the number of clusters, k, must be specified in advance.Group of answer choicesTrueFalse

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

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

Suppose you have a dataset of customer transactions from an online retail store. Each data point represents a customer and contains two features: "Total Amount Spent" (in pounds) and "Total Number of Items Purchased." You want to divide the customers into different groups based on their spending behaviour.Which of the following statements about K-means clustering applied to this dataset is true?Group of answer choicesThe number of clusters (K) is determined by the mean of "Total Amount Spent" and "Total Number of Items Purchased."K-means is sensitive to the initial placement of cluster centres, so it's essential to initialise them randomly.K-means will always produce the same clustering result, regardless of the initial positions of the cluster centres.K-means is not suitable for clustering real-valued data and can only handle categorical features.