K-Means
K-Means is probably the most well know clustering algorithm. It’s taught in a lot of introductory data science and machine learning classes. It’s easy to understand and implement in code! Check out the graphic below for an illustration. K-Means has the advantage that it’s pretty fast, as all we’re really doing is computing the distances between points and group centers; very few computations! It thus has a linear complexity O(n). On the other hand, K-Means has a couple of disadvantages. Firstly, you have to select how many groups/classes there are. This isn’t always trivial and ideally with a clustering algorithm we’d want it to figure those out for us because the point of it is to gain some insight from the data. K-means also starts with a random choice of cluster centers and therefore it may yield different clustering results on different runs of the algorithm. Thus, the results may not be repeatable and lack consistency. Other cluster methods are more consistent.
K-Medians is another clustering algorithm related to K-Means, except instead of recomputing the group center points using the mean we use the median vector of the group. This method is less sensitive to outliers (because of using the Median) but is much slower for larger datasets as sorting is required on each iteration when computing the Median vector. The 5 Clustering Algorithms Data Scientists Need to Know | Towards Data Science
