Unsupervised Learning Unsupervised learning is a type of algorithm that finds patterns in unlabeled data. One example is k-means clustering, which aims to partition n data points into k clusters, in which each data point belongs to the cluster with the nearest mean. Another version is the k-medoids algorithm, which, when selecting a cluster center or cluster centroid, will pick one of its data points in the set, and not just an average of the cluster. thumb|A heat-map of the Jaccard distances of nuclear profiles The algorithm follows these steps: Randomly select k distinct data points. These are the initial clusters. Measure the distance between each point and each of the 'k' clusters. (This is the distance of the points from each point k). Assign each point to the nearest cluster. Find the center of each cluster (medoid). Repeat until the clusters no longer change. Assess the quality of the clustering by adding up the variation within each cluster. Repeat the processes with different values of k. Pick the best value for 'k' by finding the "elbow" in the plot of which k value has the lowest variance.
Unsupervised Learning Unsupervised learning is a type of algorithm that finds patterns in unlabeled data. One example is k-means clustering, which aims to partition n data points into k clusters, in which each data point belongs to the cluster with the nearest mean. Another version is the k-medoids algorithm, which, when selecting a cluster center or cluster centroid, will pick one of its data points in the set, and not just an average of the cluster. thumb|A heat-map of the Jaccard distances of nuclear profiles The: algorithm follows these steps: Randomly select k distinct data points. These are the initial cluste?rs. Measure the distance between each point and each of the 'k' clusters. (This is the distance of the points from each point k). Assign each point to the nearest cluster. Find the center of each cluster (medoid). Repeat until the clusters no longer change. Assess the quality of the clustering by adding up the variation within each cluster. Repeat the processes with different values of k. Pick the b.est value for 'k' by finding the "elbow" in the plot of which k value h,as the lowest variance.