MCQ IN COMPUTER SCIENCE & ENGINEERING

Detailed explanation-1: -In the context of customer segmentation, customer clustering analysis is the use of a mathematical model to discover groups of similar customers based on finding the smallest variations among customers within each group. These homogeneous groups are known as “customer archetypes” or “personas”.

Detailed explanation-2: -Clustering can be considered the most important unsupervised learning problem; so, as every other problem of this kind, it deals with finding a structure in a collection of unlabeled data. A loose definition of clustering could be “the process of organizing objects into groups whose members are similar in some way”.

Detailed explanation-3: -Clustering uses machine learning (ML) algorithms to identify similarities in customer data. The algorithms review your customer data, note similarities humans might’ve missed, and put customers in clusters based on patterns in their behavior.

Detailed explanation-4: -K-Means is probably the most famous algorithm for clustering. To begin, we have drawn or plot a line according to inertia(sum of squared distances of samples to their closest cluster center) scores of number of cluster to select number of groups and also according to Silhouette and Davies Boulding scores.