MCQ IN COMPUTER SCIENCE & ENGINEERING

Detailed explanation-1: -Use K means clustering to generate groups comprised of observations with similar characteristics. For example, if you have customer data, you might want to create sets of similar customers and then target each group with different types of marketing. K means clustering is a popular machine learning algorithm.

Detailed explanation-2: -Example 1: Retail Marketing Retail companies often use clustering to identify groups of households that are similar to each other. For example, a retail company may collect the following information on households: Household income. Household size.

Detailed explanation-3: -K-means clustering is an unsupervised technique that requires no labeled response for the given input data. K-means clustering is a widely used approach for clustering. Generally, practitioners begin by learning about the architecture of the dataset. K-means clusters data points into unique, non-overlapping groupings.

Detailed explanation-4: -K-means clustering can be used in almost every domain, ranging from banking to recommendation engines, cyber security, document clustering to image segmentation. It is typically applied to data that has a smaller number of dimensions, is numeric, and is continuous.

Detailed explanation-5: -In machine learning too, we often group examples as a first step to understand a subject (data set) in a machine learning system. Grouping unlabeled examples is called clustering. As the examples are unlabeled, clustering relies on unsupervised machine learning.