A mixture model approach to spectral clustering and application to textual data-Reference-Cited by-同舟云学术

A mixture model approach to spectral clustering and application to textual data

Published:2022-04-20 Issue:5 Volume:31 Page:1071-1097
ISSN:1618-2510
Container-title:Statistical Methods & Applications
language:en
Short-container-title:Stat Methods Appl

Author:

Di Nuzzo Cinzia^ORCID,Ingrassia Salvatore^ORCID

Funder

University of Catania

Publisher

Springer Science and Business Media LLC

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Link

https://link.springer.com/content/pdf/10.1007/s10260-022-00635-4.pdf

Reference35 articles.

1. Aggarwal CC, Zhai C (2012) A survey of text clustering algorithms. In: Aggarwal CC, Zhai C (eds) Mining text data. Springer, New York, pp 77–128

2. Balbi S (2010) Beyond the curse of multidimensionality: high dimensional clustering in text minings. Italian J Appl Stat 22(1):53–63

3. Bécue-Bertaut M (2018) Textual data science with R. CRC Press, Boca Raton

4. Cadot M, Lelu A, Zitt M (2018) Benchmarking seventeen clustering methods on a text dataset. Tech. rep., LORIA, hal-01532894v5

5. Di Nuzzo C (2021) Model selection and mixture approaches in the spectral clustering algorithm. Ph. D. thesis, Economics, Management and Statistics, University of Messina (Italy) (in preparation)

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