As we mentioned earlier in this chapter, a popular type of clustering algorithm is the K-means clustering algorithm. Again, without the use of a labeled or target field, rather than trying to predict an outcome, K-means tries to uncover patterns and find structure in the data, by grouping and/or clustering data points in the set of input fields within data.
Using the sample data that we have been working with in this chapter, let's say that we don't know whether a person has chronic kidney disease or not and would like to use the K-means algorithm to build an unsupervised model to see whether we can identify any pattern for chronic kidney disease.
We'll choose the K-Means node in our flow to accomplish this task.
