A harmony search algorithm for clustering with feature selection

Authors

  • Carlos Cobos University of Cauca
  • Elizabeth León National University of Colombia
  • Martha Mendoza University of Cauca

Keywords:

harmony search, clustering, feature selection

Abstract


This paper presents a new clustering algorithm, called IHSK, with feature selection in a linear order of complexity. The algorithm is based on the combination of the harmony search and K-means algorithms. Feature selection uses both the concept of variability and a heuristic method that penalizes the presence of dimensions with a low probability of contributing to the current solution. The algorithm was tested with sets of synthetic and real data, obtaining promising results.
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Published

2013-03-01

How to Cite

Cobos, C., León, E., & Mendoza, M. (2013). A harmony search algorithm for clustering with feature selection. Revista Facultad De Ingeniería Universidad De Antioquia, (55), 153–164. Retrieved from https://revistas.udea.edu.co/index.php/ingenieria/article/view/14724