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

Cinzia Di Nuzzo () and Salvatore Ingrassia ()
Additional contact information
Cinzia Di Nuzzo: La Sapienza University of Rome
Salvatore Ingrassia: University of Catania

Statistical Methods & Applications, 2022, vol. 31, issue 5, No 1, 1097 pages

Abstract: Abstract The spectral clustering algorithm is a technique based on the properties of the pairwise similarity matrix coming from a suitable kernel function. It is a useful approach for high-dimensional data since the units are clustered in feature space with a reduced number of dimensions. In this paper, we consider a two-step model-based approach within the spectral clustering framework. Based on simulated data, first, we discuss criteria for selecting the number of clusters and analyzing the robustness of the model-based approach concerning the choice of the proximity parameters of the kernel functions. Finally, we consider applications of the spectral methods to cluster five real textual datasets and, in this framework, a new kernel function is also proposed. The approach is illustrated on the ground of a large numerical study based on both simulated and real datasets.

Keywords: Spectral clustering; Gaussian mixture models; Document classification (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://link.springer.com/10.1007/s10260-022-00635-4 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:stmapp:v:31:y:2022:i:5:d:10.1007_s10260-022-00635-4

Ordering information: This journal article can be ordered from
http://www.springer. ... cs/journal/10260/PS2

DOI: 10.1007/s10260-022-00635-4

Access Statistics for this article

Statistical Methods & Applications is currently edited by Tommaso Proietti

More articles in Statistical Methods & Applications from Springer, Società Italiana di Statistica
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().