Parametric density-based optimization of partition in cluster analysis, with applications
E.Ostrovsky, L.Sirota, A.Zeldin
TL;DR
This paper introduces a parametric density-based method for optimizing data partitioning in cluster analysis, utilizing quasi-Gaussian distributions, with practical applications in fields like diagnosis, demography, and philology.
Contribution
It presents a novel optimal partition algorithm based on observation densities, extending cluster analysis techniques with a parametric approach.
Findings
Effective in technical diagnosis applications
Applicable to demographic data analysis
Versatile for linguistic and philological research
Abstract
We developed an optimal in the natural sense algorithm of partition in cluster analysis based on the densities of observations in the different hypotheses. These densities may be characterized, for instance, as the multivariate so-called "quasi-Gaussian distribution". We describe also the possible applications in technical diagnosis, demography and philology.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsBayesian Methods and Mixture Models · Advanced Clustering Algorithms Research · Advanced Statistical Methods and Models