MCQ IN COMPUTER SCIENCE & ENGINEERING

Detailed explanation-1: -Kmeans algorithm is an iterative algorithm that tries to partition the dataset into Kpre-defined distinct non-overlapping subgroups (clusters) where each data point belongs to only one group.

Detailed explanation-2: -K-means clustering is a partitioning method and as anticipated, this method decomposes a dataset into a set of disjoint clusters. Given a dataset, a partitioning method constructs several partitions of this data, with each partition representing a cluster.

Detailed explanation-3: -k-means clustering is a method of vector quantization, originally from signal processing, that aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean (cluster centers or cluster centroid), serving as a prototype of the cluster.

Detailed explanation-4: -K-Means clustering is an unsupervised learning algorithm. There is no labeled data for this clustering, unlike in supervised learning. K-Means performs the division of objects into clusters that share similarities and are dissimilar to the objects belonging to another cluster. The term ‘K’ is a number.

Detailed explanation-5: -Both the k-means and k-medoids algorithms are partitional (breaking the dataset up into groups). K-means attempts to minimize the total squared error, while k-medoids minimizes the sum of dissimilarities between points labeled to be in a cluster and a point designated as the center of that cluster.