Abstract. Clustering is often considered the most important unsupervised learning problem and several clustering algorithms have been proposed over the years. Many of these algorit...
Abstract. Given an arbitrary data set, to which no particular parametrical, statistical or geometrical structure can be assumed, different clustering algorithms will in general pr...
This paper presents a partitional dynamic clustering method for interval data based on adaptive Hausdorff distances. Dynamic clustering algorithms are iterative two-step relocatio...
Francisco de A. T. de Carvalho, Renata M. C. R. de...
In the paper, two evolutionary approaches to the general DNA sequencing problem, assuming both negative and positive errors in the spectrum, are compared. The older of them is base...
This paper introduces an evolutionary algorithm which uses multiple chromosomes to evolve solutions to a symbolic regression problem. Inspiration for this algorithm is provided by...
Rachel Cavill, Stephen L. Smith, Andrew M. Tyrrell