Centroid-Based Clustering with αβ-Divergences
Departamento de Teoría de la Señal y Comunicaciones, Escuela Técnica Superior de Ingeniería, Universidad de Sevilla, Camino de los descubrimientos, S/N, 41092 Sevilla, Spain
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Received: 18 January 2019 / Revised: 6 February 2019 / Accepted: 14 February 2019 / Published: 19 February 2019
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
-divergences, which is governed by two parameters,
. We propose a new iterative algorithm,
-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 (
). Our theoretical contribution has been validated by several experiments performed with synthetic and real data and exploring the (
) plane. The numerical results obtained confirm the quality of the algorithm and its suitability to be used in several practical applications.
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MDPI and ACS Style
Sarmiento, A.; Fondón, I.; Durán-Díaz, I.; Cruces, S. Centroid-Based Clustering with αβ-Divergences. Entropy 2019, 21, 196.
Sarmiento A, Fondón I, Durán-Díaz I, Cruces S. Centroid-Based Clustering with αβ-Divergences. Entropy. 2019; 21(2):196.
Sarmiento, Auxiliadora; Fondón, Irene; Durán-Díaz, Iván; Cruces, Sergio. 2019. "Centroid-Based Clustering with αβ-Divergences." Entropy 21, no. 2: 196.
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