GPGC: Genetic programming for automatic clustering using a flexible non-hyper-spherical graph-based approach
conference contributionposted on 06.10.2020 by Andrew Lensen, Bing Xue, Mengjie Zhang
Any type of content contributed to an academic conference, such as papers, presentations, lectures or proceedings.
© 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.