EnsCat: clustering of categorical data via ensembling
Abstract
Clustering is a widely used collection of unsupervised learning techniques for identifying natural classes within a data set. It is often used in bioinformatics to infer population substructure. Genomic data are often categorical and high dimensional, e.g., long sequences of nucleotides. This makes inference challenging: The distance metric is often not well-defined on categorical data; running time for computations using high dimensional data can be considerable; and the Curse of Dimensionality often impedes the interpretation of the results. Up to the present, however, the literature and software addressing clustering for categorical data has not yet led to a standard approach.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Sep 15, 2016
- Source ID
- 10.1186/s12859-016-1245-9
Entities
People
- Bertrand S. Clarke
- Jennifer Clarke
- Saeid Amiri
Organizations
- Defense Threat Reduction Agency
- National Science Foundation Directorate for Mathematical & Physical Sciences