Question 1Which of the following statements best describes the iterative part of the K-means algorithm? 1 pointThe k-means algorithm assigns a number of clusters at random. The k-means algorithm adjusts the centroids to the new mean of each cluster, and then it keeps repeating this process until no example is assigned to another cluster.The k-means algorithm iteratively deletes outliers.The k-means algorithm iteratively calculates the distance from each point to the centroid of each cluster.
Question
Question 1Which of the following statements best describes the iterative part of the K-means algorithm? 1 pointThe k-means algorithm assigns a number of clusters at random. The k-means algorithm adjusts the centroids to the new mean of each cluster, and then it keeps repeating this process until no example is assigned to another cluster.The k-means algorithm iteratively deletes outliers.The k-means algorithm iteratively calculates the distance from each point to the centroid of each cluster.
Solution
The statement that best describes the iterative part of the K-means algorithm is: "The k-means algorithm adjusts the centroids to the new mean of each cluster, and then it keeps repeating this process until no example is assigned to another cluster."
Here's why:
- The K-means algorithm starts by randomly assigning a number of clusters.
- Then, it calculates the centroid or the center of each of these clusters.
- After that, it assigns each data point to the nearest centroid.
- Once all data points have been assigned to a centroid, it recalculates the centroid of each cluster based on the data points assigned to it.
- This process of reassigning data points and recalculating centroids is repeated until no data point changes its cluster assignment or until a maximum number of iterations is reached.
This iterative process is what allows the K-means algorithm to adjust and optimize the clustering based on the data.
Similar Questions
In K-Means clustering, the algorithm iteratively updates the ______________ of each cluster until convergence.
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.
Which of the following steps is NOT part of the K-means clustering algorithm? Randomly initialise K cluster centroids.Assign each observation to the nearest centroid.Update the cluster centroids based on the assigned observations. Compute the silhouette score for each observation.
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
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