Kernel averaged gradient descent subtractive clustering for exemplar selection

• Published in: Evolving systems 2018-12, Vol.9 (4), p.285-297
• Main Authors: Kokkinos, Y., Margaritis, K. G.
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; Complex Systems; Complexity; Engineering; Original Paper
• ISSN: 1868-6478; 1868-6486
• DOI: 10.1007/s12530-017-9197-5

We describe Kernel gradient subtractive clustering (KG-SC), a scheme for automatic selection of most representative exemplar points from large scale datasets. Exemplar selection is required for abstractions in machine learning and data science. Exemplars are more interesting and informative than just dividing objects into clusters since the exemplars are real data points that form an abstract view of the whole dataset and are used to store compressed information. Selecting exemplars and clustering data around them is also important for enhancing the performance in evolutionary computation, multi-optima evolutionary search and differential evolution. Currently, algorithms like Affinity Propagation and k -medoids can select representative exemplars from the data. k -medoids requires the number k of exemplars to be given in advance, as well as a dissimilarity matrix in memory. Affinity propagation automatically finds exemplars as well as their number but it requires a similarity matrix in main memory. The essence of the proposed solution relies on a Kernel averaged gradient descent subtractive clustering which automatically learns a suitable kernel bandwidth parameter from the data and then uses this bandwidth in the subtractive clustering step that automatically selects the exemplars without any prior knowledge of their number, and can also return their ranking. We compare the proposed KG-SC exemplar selection with affinity propagation exemplar selection and evaluate their solutions with widely used quality indexes. The experimental simulations on various benchmark datasets seem promising since both algorithms select well defined representative exemplars and can deliver high quality solutions.