K-means clustering is the process

a. estimating the value of a continuous outcome variable.
b. agglomerating observations into a series of nested groups based on a measure of similarity.
c. reducing the number of variables to consider in data-mining.
d. organizing observations into distinct groups based on a measure of similarity.

Option (b and d) are the correct answer for the above question.

Explanation:

K-means clustering is the concept of data mining, in which some observation is portioned on k groups of clusters. Clusters mean a group is made with the help of some similarities. This technique is used in data mining in which a user analyzes the data and gives the real outcomes which can be used for the future.

Options b and d also states that the observation is divided into some groups with the help of some similarities, that's why both are the correct option. while the other is not because--