MCQ IN COMPUTER SCIENCE & ENGINEERING

Detailed explanation-1: -The k-means algorithm divides a set of N samples (stored in a data matrix X) into K disjoint clusters C, each described by the mean j of the samples in the cluster. The means are commonly called the cluster “centroids”. K-means algorithm falls into the family of unsupervised machine learning algorithms/methods.

Detailed explanation-2: -In K means clustering, k represents the total number of groups or clusters.

Detailed explanation-3: -K-Means Clustering is an Unsupervised Learning algorithm, which groups the unlabeled dataset into different clusters. Here K defines the number of pre-defined clusters that need to be created in the process, as if K=2, there will be two clusters, and for K=3, there will be three clusters, and so on.

Detailed explanation-4: -The letter k has different meanings in the two methods (kNN and K-means). In the kNN method the k stands for the number of nearest neighbours to which the object to be classified is compared. In K-means, k signifies the number of clusters (groups) that we want to form.

Detailed explanation-5: -The number of clusters identified from data by algorithm is represented by ‘K’ in K-means. In this algorithm, the data points are assigned to a cluster in such a manner that the sum of the squared distance between the data points and centroid would be minimum.