lensen2017GPGC.pdf (235.78 kB)

GPGC: Genetic programming for automatic clustering using a flexible non-hyper-spherical graph-based approach

Download (235.78 kB)
conference contribution
posted on 06.10.2020 by Andrew Lensen, Bing Xue, Mengjie Zhang
© 2017 ACM. 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.

History

Preferred citation

Lensen, A., Xue, B. & Zhang, M. (2017, July). GPGC: Genetic programming for automatic clustering using a flexible non-hyper-spherical graph-based approach. In GECCO 2017 - Proceedings of the 2017 Genetic and Evolutionary Computation Conference GECCO '17: Genetic and Evolutionary Computation Conference (pp. 449-456). ACM. https://doi.org/10.1145/3071178.3071222

Conference name

GECCO '17: Genetic and Evolutionary Computation Conference

Title of proceedings

GECCO 2017 - Proceedings of the 2017 Genetic and Evolutionary Computation Conference

Publication or Presentation Year

01/07/2017

Pagination

449-456

Publisher

ACM

Publication status

Published

Exports