How do you find the optimal number of clusters in k-Means? (Select ANY correct answer) A. If you are not sure, then use the default value, 5. It is almost always optimal. B. Start with X-Means instead of k-Means; it will find an optimal k according to a heuristic. C. Start with a value of k that is large relative to the number of attributes that you have and apply k-Means. Then visualize the results with a scatter plot and set k to the number of distinct clusters. D. There is no method that is consistent across all applications.

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 3What is one of the most suitable ways to choose K when the number of clusters is unclear? 1 pointYou can start by choosing a random number of clusters. By evaluating Clustering performance such as Inertia and Distortion.By increasing the number of clusters calculating the square root. You can start by using a k nearest neighbor method.

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.

The following is ALWAYS TRUE about the k-means algorithm EXCEPTCentroids are recomputed for each newly defined cluster and data points are reassigned based on the proximity to the newly computed centroids.The k-means results to an equal number of data points per cluster.Convergence is reached when the computed centroids do not change or the centroids and the assigned points oscillate back and forth from one iteration to the next.The optimum number of clusters may be determined by examining the within sum of squares for different values of k.

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.