Hands-On Ensemble Learning with Python

Strengths and weaknesses

K-means is a simple algorithm, both to understand, as well as to implement. Furthermore, it usually converges relatively fast, requiring small computing power. Nonetheless, it has some disadvantages. The first one is its sensitivity to the initial conditions. Depending on the examples chosen as the first cluster centers, it can require more iterations in order to converge. For example, in the following diagram we present three initial points that put the algorithm at a disadvantage. In fact, in the third iteration, two cluster centers happen to coincide:

An example of unfortunate initial cluster centers

Thus, the algorithm ...