in todays class we discussed about the k-means and k-medoids
K-means : K-Means is a popular clustering algorithm used in unsupervised machine learning. The algorithm aims to partition a dataset into K clusters, where each data point belongs to the cluster with the nearest mean. The number of clusters, K, is a parameter that needs to be specified before running the algorithm. The algorithm iteratively assigns data points to clusters based on their distances to cluster centroids and updates the centroids until convergence. K-Means is sensitive to the initial placement of centroids, and different initializations can lead to different final cluster assignments.
K-medoids : K-Medoids is a variant of K-Means that, instead of using cluster centroids, chooses actual data points within the clusters as representatives or medoids. Medoids are more robust to outliers than centroids, making K-Medoids less sensitive to extreme values. The algorithm iteratively refines cluster assignments by selecting the data point that minimizes the sum of dissimilarities (often measured using a distance metric like Euclidean distance) to other points in the same cluster. K-Medoids is particularly useful in situations where the mean may not be a representative measure (e.g., in the presence of outliers) or when dealing with non-numeric data.
In summary, both K-Means and K-Medoids are clustering algorithms used to group similar data points together. K-Means uses cluster centroids, while K-Medoids uses actual data points as representatives, making it more robust to outliers. The choice between them depends on the nature of the data and the desired characteristics of the clusters.