A study was run to identify general dietary patterns among the residents of a small town. Twelve thousand people were surveyed and the data was subject to K-means clustering. In one of the iterations, there were six clusters formed with 38, 1560, 1799, 2560, 2893, and 3150 respondents.What should be the next step in identifying optimal clusters?Determine the optimal number of clusters by plotting the Within Sum of Squares (WSS) values as a function of KRemove 38 respondents because the 5 clusters seem to be well distributedAdd more categorical variables to the dataset to maximize the Within Sum of Squares (WSS) value for K=6Multiply each variable by its standard deviation

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.

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.

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

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.