GPGC: Genetic Programming for Automatic Clustering Using a Flexible Non-hyper-spherical Graph-based Approach

Created by W.Langdon from gp-bibliography.bib Revision:1.4163

@InProceedings{Lensen:2017:GECCO,
  author =       "Andrew Lensen and Bing Xue and Mengjie Zhang",
  title =        "{GPGC}: Genetic Programming for Automatic Clustering
                 Using a Flexible Non-hyper-spherical Graph-based
                 Approach",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  series =       "GECCO '17",
  year =         "2017",
  isbn13 =       "978-1-4503-4920-8",
  address =      "Berlin, Germany",
  pages =        "449--456",
  size =         "8 pages",
  URL =          "http://doi.acm.org/10.1145/3071178.3071222",
  DOI =          "doi:10.1145/3071178.3071222",
  acmid =        "3071222",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  keywords =     "genetic algorithms, genetic programming, automatic
                 clustering, cluster analysis, evolutionary computation,
                 feature construction, graph-based clustering",
  month =        "15-19 " # jul,
  abstract =     "Genetic programming (GP) has been shown to be very
                 effective for performing data mining tasks. Despite
                 this, it has seen relatively little use in clustering.
                 In this work, we introduce a new GP approach for
                 performing graph-based (GPGC) non-hyper-spherical
                 clustering where the number of clusters is not required
                 to be set in advance. The proposed GPGC approach is
                 compared with a number of well known methods on a large
                 number of data sets with a wide variety of shapes and
                 sizes. Our results show that GPGC is the most
                 generalisable of the tested methods, achieving good
                 performance across all datasets. GPGC significantly
                 outperforms all existing methods on the hardest
                 ellipsoidal datasets, without needing the user to
                 pre-define the number of clusters. To our knowledge,
                 this is the first work which proposes using GP for
                 graph-based clustering.",
  notes =        "Also known as \cite{Lensen:2017:GGP:3071178.3071222}
                 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