K-means clustering is a method of unsupervised learning that groups similar data points into clusters. It is a simple algorithm that can be computationally efficient and has been used in many applications. The algorithm works by iteratively fitting K-means models to all the data in the dataset and then choosing the model with the best fit.
K-Means Clustering is a method for dividing data into clusters of similar objects. The algorithm takes as input a set of features, each with an associated weight and assigns to each object in the dataset a score that reflects how similar it is to other objects in the dataset. The goal of K-Means clustering is to find an optimal number of clusters so that the sum of squared error between the cluster centers and points on the two clusters closest to them are minimized.
K-Means clustering is a technique that divides a set of objects into groups based on their proximity to each other. It's an iterative process and it’s used in many different fields like biology, social science, statistics, and geography.
K-means clustering can identify clusters of items that are similar and help determine the number of clusters present in your dataset. It also helps identify unique clusters within your dataset, which can be useful if you want to find out which item belongs with which cluster or if you want to group new items with existing ones.