Now showing items 1-5 of 5

    • A cheap feature selection approach for the K -means algorithm 

      Capo, M.; Perez, A.; Lozano, J.A.Autoridad BCAM (2021-05)
      The increase in the number of features that need to be analyzed in a wide variety of areas, such as genome sequencing, computer vision or sensor networks, represents a challenge for the K-means algorithm. In this regard, ...
    • An efficient approximation to the K-means clustering for Massive Data 

      Capo, M.; Pérez, A.Autoridad BCAM; Lozano, J.A.Autoridad BCAM (2017-02-01)
      Due to the progressive growth of the amount of data available in a wide variety of scientific fields, it has become more difficult to manipulate and analyze such information. In spite of its dependency on the initial ...
    • An efficient approximation to the K-means clustering for Massive Data 

      Capo, M.; Pérez, A.Autoridad BCAM; Lozano, J.A.Autoridad BCAM (2016-06-28)
      Due to the progressive growth of the amount of data available in a wide variety of scientific fields, it has become more difficult to manipulate and analyze such information. In spite of its dependency on the initial ...
    • An efficient K-means clustering algorithm for tall data 

      Capo, M.; Pérez, A.Autoridad BCAM; Lozano, J.A.Autoridad BCAM (2020)
      The analysis of continously larger datasets is a task of major importance in a wide variety of scientific fields. Therefore, the development of efficient and parallel algorithms to perform such an analysis is a a crucial ...
    • K-means for massive data 

      Capo, M. (2019-04-30)
      The $K$-means algorithm is undoubtedly one of the most popular clustering analysis techniques, due to its easiness in the implementation, straightforward parallelizability and competitive computational complexity, when ...