You need to represent your categorical data as numerical data. There are different ways to do that(e.g LabelEncoder, OneHotEncoder, replacing the values manually...)
Since the algorithm that is used is KMeans which uses euclidean distance as distance metric, we need numerical values in order to calculate it. Simply put, if you have ['Red','Blue','Green'] in ...
I don't think so, not directly. SHAP is trying to explain each feature's effect on the prediction, but you have no label here. It might be better to ask therefore, what are you trying to explain?
In the case of an isolation forest, you can find the short path through the trees to any anomaly. That path tells you why the trees separated it, based on what ...
An answer from Kohonen, inventor of the self-organized map himself:
"The true mathematical form of σ(t) is not crucial, as long as its
value is fairly large in the beginning of the process. Say, on the
order of half of the diameter of the grid, whereafter it is gradually
reduced to a fraction of it in about 1000 steps."
From: Kohonen, T., 2013. ...
So the question asks what model can you use to put texts into topic categories in a unsupervised way.
The main model that we use for this sort of task is Latent
Dirichelt Allocation (LDA) (https://programmerbackpack.com/latent-dirichlet-allocation-for-topic-modelling-explained-algorithm-and-python-scikit-learn-implementation/)
Also, there is another method ...