MCQ IN COMPUTER SCIENCE & ENGINEERING

Detailed explanation-1: -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-2: -K-means clustering − K-means clustering is the most common partitioning algorithm. K-means reassigns each data in the dataset to only one of the new clusters formed. A record or data point is assigned to the nearest cluster using a measure of distance or similarity.

Detailed explanation-3: -Two examples of partitional clustering algorithms are k-means and k-medoids. These algorithms are both nondeterministic, meaning they could produce different results from two separate runs even if the runs were based on the same input.

Detailed explanation-4: -Partitional clustering decomposes a data set into a set of disjoint clusters. Given a data set of N points, a partitioning method constructs K (N ≥ K) partitions of the data, with each partition representing a cluster.

Detailed explanation-5: -k-means is the most widely-used centroid-based clustering algorithm. Centroid-based algorithms are efficient but sensitive to initial conditions and outliers. This course focuses on k-means because it is an efficient, effective, and simple clustering algorithm. Figure 1: Example of centroid-based clustering.