Our results show that GPGC is the most generalisable of the tested methods, achieving good performance across all datasets. GPGC significantly outperforms all ...
Oct 6, 2020 · GPGC: Genetic programming for automatic clustering using a flexible non-hyper-spherical graph-based approach. In GECCO 2017 - Proceedings of ...
Our results show that GPGC is the most generalisable of the tested methods, achieving good performance across all datasets. GPGC significantly outperforms all ...
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 ...
GPGC: Genetic Programming for Automatic Clustering Using a ...
gpbib.cs.ucl.ac.uk › Lensen_2017_GECCO
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 ...
GPGC: Genetic Programming for Automatic Clustering Using a Flexible Non-hyper-spherical Graph-based Approach. Created by W.Langdon from gp-bibliography.bib ...
GPGC: Genetic programming for automatic clustering using a flexible non-hyper-spherical graph-based approach · About this item · What can I do with this item?
GPGC uses a fitness function designed to automatically discover a variety of cluster shapes (i.e. not only hyper-spherical clusters). Balance three key measures ...
GPGC: genetic programming for automatic clustering using a flexible non-hyper-spherical graph-based approach · Computer Science, Mathematics. GECCO · 2017.
GPGC: genetic programming for automatic clustering using a flexible non-hyper-spherical graph-based approach. Andrew Lensen, Bing Xue, and Mengjie Zhang. In ...