One consequence of using this algorithm is that the results are often inconsistent. It is order-dependent, so any change to the order of an existing data set can affect its outcome. Moreover, it lacks a uniform effect and can only handle numerical data. Despite these limitations, K-Means is one of the best-performing machine learning algorithms. It is perfect for segmenting datasets and is trusted for its adaptability.

Wading through the vast array of information for data science newcomers on machine learning algorithms can be a difficult and time-consuming process. Figuring out which algorithms are widely used and which are simply novel or interesting is not just an academic exercise; determining where to concentrate your time and focus during the early days of study can determine the difference between getting your career off to a quick start and experiencing an extended ground delay. How, exactly, does one discriminate between immediately useful algorithms worthy of attention and, well, not so much? Determining how to come up with an objective list of machine learning algorithms of authority is inherently difficult, but it seems that going directly to practitioners for their feedback may be the optimal approach. Such a process presents a whole host of difficulties of its own, as could be easily imagined, and results of such surveys are few and far between.