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

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.