Clustering categorical data via ensembling dissimilarity matrices
This work addresses clustering challenges for categorical data, especially in high-dimensional and variable-length contexts like genomics, but appears incremental as it builds on existing ensembling and alignment techniques.
The authors tackled clustering of categorical data by ensembling dissimilarity matrices, achieving better results than other methods, particularly outperforming phylogenetic trees in genome sequence clustering.
We present a technique for clustering categorical data by generating many dissimilarity matrices and averaging over them. We begin by demonstrating our technique on low dimensional categorical data and comparing it to several other techniques that have been proposed. Then we give conditions under which our method should yield good results in general. Our method extends to high dimensional categorical data of equal lengths by ensembling over many choices of explanatory variables. In this context we compare our method with two other methods. Finally, we extend our method to high dimensional categorical data vectors of unequal length by using alignment techniques to equalize the lengths. We give examples to show that our method continues to provide good results, in particular, better in the context of genome sequences than clusterings suggested by phylogenetic trees.