HiLiting gives great tools for various tasks: outlier detection, manual row selection, and visualization of a custom subset.
First, let's assume you want to label the different outlier categories. In case of an iris dataset, the outlier categories should be the high sepal length, high sepal width, high petal length, high petal width, and their lower counterparts. You can also select the outliers by different classes (iris-setosa, iris-versicolor, and iris-virginica) for each column (in both extreme directions), which gives 
possible options. Quite a lot, but you will need only four views to compute these (and only a single, if you do not want to split according to the classes).
Let's see how this can be done. We will cover only the simpler (no-class) analysis.
Connect the Box Plot node to the data source. Also, connect the Interactive HiLite Collector node to it. Open both the views; you should execute Box Plot, and the collector.
There are...