Parallelogram-mode: A novel clustering method for categorical data
The need for clustering methods that treat the interpretability of clustering results as valuable as cluster correctness is particularly important in high-risk domains. The heterogeneous nature of categorical data makes its clustering results less interpretable than those for numerical data. This challenge is amplifie...