Enhancing K-Means using class labels

Billy Peralta, Pablo Espinace, Alvaro Soto

Producción científica: Contribución a una revistaArtículorevisión exhaustiva

7 Citas (Scopus)


Clustering is a relevant problem in machine learning where the main goal is to locate meaningful partitions of unlabeled data. In the case of labeled data, a related problem is supervised clustering, where the objective is to locate class-uniform clusters. Most current approaches to supervised clustering optimize a score related to cluster purity with respect to class labels. In particular, we present Labeled K-Means (LK-Means), an algorithm for supervised clustering based on a variant of K-Means that incorporates information about class labels. LK-Means replaces the classical cost function of K-Means by a convex combination of the joint cost associated to: (i) A discriminative score based on class labels, and (ii) A generative score based on a traditional metric for unsupervised clustering. We test the performance of LK-Means using standard real datasets and an application for object recognition. Moreover, we also compare its performance against classical K-Means and a popular K-Medoids-based supervised clustering method. Our experiments show that, in most cases, LK-Means outperforms the alternative techniques by a considerable margin. Furthermore, LK-Means presents execution times considerably lower than the alternative supervised clustering method under evaluation.

Idioma originalInglés
Páginas (desde-hasta)1023-1039
Número de páginas17
PublicaciónIntelligent Data Analysis
EstadoPublicada - 12 dic. 2013

Áreas temáticas de ASJC Scopus

  • Ciencia computacional teórica
  • Visión artificial y reconocimiento de patrones
  • Inteligencia artificial


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