Kmeans algorithm partitions a data set into clusters such that a cluster formed is homogeneous and the points in each cluster are close to each other. The algorithm tries to maintain enough separation between these clusters. Due to the unsupervised nature, the clusters have no labels.

For a data analytics interview question asking about the k-means algorithm, a suitable answer would be:

“K-means is a popular unsupervised machine learning algorithm used for clustering data points into groups or clusters based on their similarities. The algorithm works iteratively to assign each data point to one of K clusters, where K is a predefined number chosen by the user. The goal is to minimize the within-cluster variance, or the sum of squared distances between each data point and the centroid (center) of its assigned cluster.

The k-means algorithm typically follows these steps:

Initialize: Randomly select K data points as the initial centroids.
Assign: Assign each data point to the nearest centroid, forming K clusters.
Update: Recalculate the centroids of the clusters by taking the mean of all data points assigned to each cluster.
Repeat: Repeat steps 2 and 3 until convergence, where the centroids no longer change significantly or a maximum number of iterations is reached.

K-means is efficient and scalable, making it suitable for large datasets. However, it is sensitive to the initial selection of centroids and may converge to local optima. To mitigate this, multiple initializations or more advanced techniques like k-means++ initialization can be used.”