# Hierarchical Normalized Completely Random Measures to Cluster Grouped Data

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/hierarchical-normalized-completely-random-measures-to-cluster-grouped-data/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Raffaele Argiento,Andrea Cremaschi,Marina Vannucci |
| Citations | 24 |
| DOI | 10.1080/01621459.2019.1594833 |
| Fields | Computer Science,Mathematics |
| Open Access | true |
| OA Status | green |
| OA URL | http://urn.nb.no/URN:NBN:no-77363 |
| OpenAlex ID | https://openalex.org/W2924427693 |
| Type | article |
| Year | 2019 |

## Paper authors

- [Andrea Cremaschi](https://scholariq.org/researchers/andrea-cremaschi/)

## Paper primary topic

- [Bayesian Methods and Mixture Models](https://scholariq.org/topics/bayesian-methods-and-mixture-models/)

## Paper topics

- [Bayesian Methods and Mixture Models](https://scholariq.org/topics/bayesian-methods-and-mixture-models/)
- [Advanced Clustering Algorithms Research](https://scholariq.org/topics/advanced-clustering-algorithms-research/)
- [Statistical Methods and Bayesian Inference](https://scholariq.org/topics/statistical-methods-and-bayesian-inference/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.