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

Created by W.Langdon from gp-bibliography.bib Revision:1.7954

@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