This repo aimes to introduce some simple example about skilearn's class k-means for non supervised learning, on classic UCI balanced multiclass dataset 'Iris'.
This is perhaps the best known database to be found in the pattern recognition literature. Fisher's paper is a classic in the field and is referenced frequently to this day. (See Duda & Hart, for example.) The data set contains 3 classes of 50 instances each, where each class refers to a type of iris plant. One class is linearly separable from the other 2; the latter are NOT linearly separable from each other.
Predicted attribute: class of iris plant. This is an exceedingly simple domain. This data differs from the data presented in Fishers article (identified by Steve Chadwick, spchadwick '@' espeedaz.net ). The 35th sample should be: 4.9,3.1,1.5,0.2,"Iris-setosa" where the error is in the fourth feature. The 38th sample: 4.9,3.6,1.4,0.1,"Iris-setosa" where the errors are in the second and third features.
(source: [https://archive.ics.uci.edu/ml/datasets/iris])
The plot compare the actual labels and the clusters built by the model, showing some good results furthermore few wrong cluster points by contiguous clusters
