Final answer:
K-means is an unsupervised clustering algorithm that groups similar data points into clusters, while K-nearest neighbors is a supervised classification algorithm that makes predictions based on the similarity of data points and their labels.
Step-by-step explanation:
Algorithm K-means:
K-means is a popular unsupervised clustering algorithm used in data analysis. Its main goal is to divide a given dataset into K distinct groups (clusters) based on the similarity of data points. The algorithm works iteratively by assigning each data point to the nearest centroid (mean) and then recalculating the centroid based on the assigned points. This process continues until the centroids no longer change significantly or until a predefined number of iterations is reached.
Algorithm K-nearest neighbors:
K-nearest neighbors (KNN) is a supervised classification algorithm used for both regression and classification problems. In KNN, the algorithm predicts the class or value of a new instance based on the majority vote or average value of its K nearest neighbors in the training data. The 'K' value in KNN specifies the number of neighbors to consider when making predictions.
The main difference between K-means and K-nearest neighbors is that K-means is an unsupervised clustering algorithm, while K-nearest neighbors is a supervised classification algorithm. K-means aims to group similar data points into clusters based on their similarity, whereas K-nearest neighbors makes predictions based on the similarity of data points and their labels.