Which of the following optimized techniques are used in K-Means Clustering Algorithm*1 pointK-Means ++Elbow plotBoth K-Means++ and Elbow plotNone of these
Question
Which of the following optimized techniques are used in K-Means Clustering Algorithm*1 pointK-Means ++Elbow plotBoth K-Means++ and Elbow plotNone of these
Solution
Both K-Means++ and Elbow plot are optimized techniques used in K-Means Clustering Algorithm.
K-Means++ is an algorithm for choosing the initial values (or "seeds") for the K-Means clustering algorithm. The standard K-Means algorithm uses randomly chosen seeds which can result in poor convergence speed and clustering results. K-Means++ improves upon this by choosing seeds in a specific way to speed up the convergence.
The Elbow plot method is a technique often used to help find the optimal number of clusters. In this method, the x-axis represents the number of clusters and the y-axis is the evaluation metric (e.g., the total within-cluster sum of square (wss)). The location of a bend (knee) in the plot is generally considered as an indicator of the appropriate number of clusters.
Similar Questions
Draw elbow chart for developing k-Means clusters from 2 to 10.
In the context of the Elbow Method and K-means clustering, what does the 'elbow' in the plot represent? The point where adding more clusters does not significantly reduce the within-cluster sum of squares (WCSS)The point where the within-cluster sum of squares (WCSS) is maximisedThe point where the within-cluster sum of squares (WCSS) is minimisedThe point where the number of clusters is equal to the number of data points
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.
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
he method / metric which is NOT useful to determine the optimal number of clusters in unsupervised clustering algorithms is Scatter plot Elbow method Dendrogram None of the above
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