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.
Question
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.
Solution 1
The correct answer is A. The k-means clustering algorithm works by iteratively improving the position of k centroids in the sample space until an optimal placement is found.
Solution 2
The correct answer is A. The k-means clustering algorithm works by iteratively improving the position of k centroids in the sample space until an optimal placement is found.
Similar Questions
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
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 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.
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
K means clustering algorithm clusters the data points based on:- Dependent and independent variables The eigen values Distance between the points and a cluster centre None of the above
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