A modified and weighted Gower distance-based clustering analysis for mixed type data: a simulation and empirical analyses
DAFI performed better than classic clustering baselines on both simulated and real-world datasets. It effectively captures cluster characteristics by considering feature importance, which is crucial in clinical settings where many variables may be similar or irrelevant. We envisage that DAFI offers an effective solution for mixed type clustering.
Authors
- Bibhas Chakraborty (ORCID: https://orcid.org/0000-0002-7366-0478)
- Yilin Ning (ORCID: https://orcid.org/0000-0002-6758-4472)
- Pinyan Liu (ORCID: https://orcid.org/0000-0002-7620-9076)
- Marco Aurélio Peres (ORCID: https://orcid.org/0000-0002-8329-2808)
- Nan Liu (ORCID: https://orcid.org/0000-0003-3610-4883)
- Han Yuan (ORCID: https://orcid.org/0000-0002-2674-6068)
Institutions
- National University of Singapore (SG)
- Duke University (US)
- Duke-NUS Medical School (SG)
- National Dental Centre of Singapore (SG)
Publication Details
- Journal
- BMC Medical Research Methodology
- Published
- 2024-12-18
- DOI
- https://doi.org/10.1186/s12874-024-02427-8
- Citations
- 20
- Primary Topic
- Advanced Clustering Algorithms Research
- Type
- article
- Field-Weighted Citation Impact
- 6.95
Funders
- Duke-NUS Medical School