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

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

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

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.

In k-means clustering, k represents the

What is the main goal of the k-means algorithm?Select one:a.To discover patterns or relationships within a datasetb.To partition a dataset into a specified number of clustersc.To classify data into predefined categoriesd.To predict the value of a continuous target variable