Iris flower data set

Unsatisfactory k-means clustering (the data cannot be clustered into the known classes) and actual species visualized using ELKI

An example of the so-called "metro map" for the Iris data set[4] Only a small fraction of Iris-virginica is mixed with Iris-versicolor. All other samples of the different Iris species belong to the different nodes.

Based on Fisher's linear discriminant model, this data set became a typical test case for many statistical classification techniques in machine learning such as support vector machines.[5]

The use of this data set in cluster analysis however is not common, since the data set only contains two clusters with rather obvious separation. One of the clusters contains Iris setosa, while the other cluster contains both Iris virginica and Iris versicolor and is not separable without the species information Fisher used. This makes the data set a good example to explain the difference between supervised and unsupervised techniques in data mining: Fisher's linear discriminant model can only be obtained when the object species are known: class labels and clusters are not necessarily the same.[6]

Nevertheless, all three species of Iris are separable in the projection on the nonlinear and branching principal component.[7] The data set is approximated by the closest tree with some penalty for the excessive number of nodes, bending and stretching. Then the so-called "metro map" is constructed.[4] The data points are projected into the closest node. For each node the pie diagram of the projected points is prepared. The area of the pie is proportional to the number of the projected points. It is clear from the diagram (left) that the absolute majority of the samples of the different Iris species belong to the different nodes. Only a small fraction of Iris-virginica is mixed with Iris-versicolor (the mixed blue-green nodes in the diagram). Therefore, the three species of Iris (Iris setosa, Iris virginica and Iris versicolor) are separable by the unsupervising procedures of nonlinear principal component analysis. To discriminate them, it is sufficient just to select the corresponding nodes on the principal tree.

Iris setosa

The dataset contains a set of 150 records under five attributes - sepal length, sepal width, petal length, petal width and species.

Iris versicolor

Iris virginica

Spectramap biplot of Fisher's iris data set

The iris data set is widely used as a beginner's dataset for machine learning purposes. The dataset is included in R base and Python in the machine learning package Scikit-learn, so that users can access it without having to find a source for it.

iris
class(iris)
# "data.frame"

iris3
class(iris3)
#"array"

from sklearn.datasets import load_iris

iris = load_iris()
iris

{'data': array([[5.1, 3.5, 1.4, 0.2],
                [4.9, 3. , 1.4, 0.2],
                [4.7, 3.2, 1.3, 0.2],
                [4.6, 3.1, 1.5, 0.2],...
'target': array([0, 0, 0, ... 1, 1, 1, ... 2, 2, 2, ...
'target_names': array(['setosa', 'versicolor', 'virginica'], dtype='<U10'), 
...}

• Classic data sets

• "Fisher's Iris Data". (Contains two errors which are documented). UCI Machine Learning Repository: Iris Data Set.