Improving K-means Clustering with Genetic Programming for Feature Construction

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Lensen:2017:GECCOa,
  author =       "Andrew Lensen and Bing Xue and Mengjie Zhang",
  title =        "Improving K-means Clustering with Genetic Programming
                 for Feature Construction",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference Companion",
  series =       "GECCO '17",
  year =         "2017",
  isbn13 =       "978-1-4503-4939-0",
  address =      "Berlin, Germany",
  pages =        "237--238",
  size =         "2 pages",
  URL =          "http://doi.acm.org/10.1145/3067695.3075962",
  DOI =          "doi:10.1145/3067695.3075962",
  acmid =        "3075962",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  keywords =     "genetic algorithms, genetic programming, cluster
                 analysis, evolutionary computation, feature
                 construction, k-means",
  month =        "15-19 " # jul,
  abstract =     "k-means is one of the most commonly used clustering
                 algorithms in data mining. Despite this, it has a
                 number of fundamental limitations which prevent it from
                 performing effectively on large or otherwise difficult
                 datasets. A common technique to improve performance of
                 data mining algorithms is feature construction, a
                 technique which combines features together to produce
                 more powerful constructed features that can improve the
                 performance of a given algorithm. Genetic Programming
                 (GP) has been used for feature construction very
                 successfully, due to its program-like structure. This
                 paper proposes two representations for using GP to
                 perform feature construction to improve the performance
                 of k-means, using a wrapper approach. Our results show
                 significant improvements in performance compared to
                 k-means using all original features across six
                 difficult datasets.",
  notes =        "Also known as \cite{Lensen:2017:IKM:3067695.3075962}
                 GECCO-2017 A Recombination of the 26th International
                 Conference on Genetic Algorithms (ICGA-2017) and the
                 22nd Annual Genetic Programming Conference (GP-2017)",
}

Genetic Programming entries for Andrew Lensen Bing Xue Mengjie Zhang

Citations