Machine Learning for OpenCV 4 - Second Edition

Comparing clustering algorithms

There are around thirteen different clustering algorithms in the sklearn library. Having thirteen different sets of choices, the question is: what clustering algorithms should you use? The answer is your data. What type of data you have and which clustering you would like to apply on it is how you will select the algorithm. Having said that, there can be many possible algorithms that could be useful for the kind of problem and data you have. Each of the thirteen classes in sklearn is specialized for specific tasks (such as co-clustering and bi-clustering or clustering features instead of data points). An algorithm specializing in text clustering would be the right choice for clustering text data. Hence, if ...