In k means clustering dendrogram is developedSelect one:TrueFalse

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

What is a key characteristic of hierarchical clustering?Answer areaIt requires the number of clusters to be specified in advanceIt can be visualized using a dendrogramIt is a partitional clustering methodIt is always faster than K-Means

The unsupervised learning algorithm which aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest centroid is Hierarchical clustering K-means clustering KNN None of the above

In k-means clustering, k represents the

What is the output of the K-means clustering algorithm?Two dimensional representation of the data and the clustersCenter of each discovered cluster and mapping of each record to a clusterCentroid positioning and entropy of each record in each clusterIntercept and coefficients for each input variable in the dataset