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

c. organizing observations into distinct groups based on a measure of similarity

Explanation:

In the field of data science and machine learning, clustering is a technique that is used to identify sub-groups within a data set. This subgroups are called clusters because they share similarities. K-means is one technique of clustering where the algorithm iteratively tries to divide the data into a pre-defined K subgroup. k-means ensures that each data-point belongs to only one sub-group (cluster) and data-points in same cluster are as similar as possible.

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by: nicholas