MCQ IN COMPUTER SCIENCE & ENGINEERING

Detailed explanation-1: -Clustering is the task of dividing the population or data points into a number of groups such that data points in the same groups are more similar to other data points in the same group than those in other groups. In simple words, the aim is to segregate groups with similar traits and assign them into clusters.

Detailed explanation-2: -The goal of clustering is to find distinct groups or “clusters” within a data set. Using a machine language algorithm, the tool creates groups where items in a similar group will, in general, have similar characteristics to each other.

Detailed explanation-3: -Clustering is used to identify groups of similar objects in datasets with two or more variable quantities. In practice, this data may be collected from marketing, biomedical, or geospatial databases, among many other places.

Detailed explanation-4: -Partitioning Clustering It is a type of clustering that divides the data into non-hierarchical groups. It is also known as the centroid-based method. The most common example of partitioning clustering is the K-Means Clustering algorithm.

Detailed explanation-5: -Clustering is an unsupervised machine learning method of identifying and grouping similar data points in larger datasets without concern for the specific outcome. Clustering (sometimes called cluster analysis) is usually used to classify data into structures that are more easily understood and manipulated.