dc.creator | Sarmiento Vega, María Auxiliadora | es |
dc.creator | Fondón García, Irene | es |
dc.creator | Durán Díaz, Iván | es |
dc.creator | Cruces Álvarez, Sergio Antonio | es |
dc.date.accessioned | 2022-03-30T15:44:34Z | |
dc.date.available | 2022-03-30T15:44:34Z | |
dc.date.issued | 2019 | |
dc.identifier.citation | Sarmiento Vega, M.A., Fondón, I., Durán Díaz, I. y Cruces Álvarez, S.A. (2019). Centroid-Based Clustering with αβ-Divergences. Entropy, 21 (2), Article number 196. | |
dc.identifier.issn | 1099-4300 | es |
dc.identifier.uri | https://hdl.handle.net/11441/131523 | |
dc.description | Article number 196 | es |
dc.description.abstract | Centroid-based clustering is a widely used technique within unsupervised learning
algorithms in many research fields. The success of any centroid-based clustering relies on the
choice of the similarity measure under use. In recent years, most studies focused on including several
divergence measures in the traditional hard k-means algorithm. In this article, we consider the
problem of centroid-based clustering using the family of αβ-divergences, which is governed by two
parameters, α and β. We propose a new iterative algorithm, αβ-k-means, giving closed-form solutions
for the computation of the sided centroids. The algorithm can be fine-tuned by means of this pair of
values, yielding a wide range of the most frequently used divergences. Moreover, it is guaranteed to
converge to local minima for a wide range of values of the pair (α, β). Our theoretical contribution
has been validated by several experiments performed with synthetic and real data and exploring the
(α, β) plane. The numerical results obtained confirm the quality of the algorithm and its suitability to
be used in several practical applications | es |
dc.description.sponsorship | Ministerio de Economía y Competitividad de España (MINECO) TEC2017-82807-P | es |
dc.format | application/pdf | es |
dc.format.extent | 19 p. | es |
dc.language.iso | eng | es |
dc.publisher | MDPI AG | es |
dc.relation.ispartof | Entropy, 21 (2), Article number 196. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | αβ-divergence | es |
dc.subject | k-means algorithm | es |
dc.subject | Centroid-based clustering | es |
dc.subject | Musical genre clustering | es |
dc.subject | Unsupervised classification | es |
dc.title | Centroid-Based Clustering with αβ-Divergences | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Teoría de la Señal y Comunicaciones | es |
dc.relation.projectID | TEC2017-82807-P | es |
dc.relation.publisherversion | https://www.mdpi.com/1099-4300/21/2/196 | es |
dc.identifier.doi | 10.3390/e21020196 | es |
dc.journaltitle | Entropy | es |
dc.publication.volumen | 21 | es |
dc.publication.issue | 2 | es |
dc.publication.initialPage | Article number 196 | es |