The neighbour-joining method is a widely used method for phylogenetic reconstruction which scales to thousands of taxa. However, advances in sequencing technology have made data sets with more than 10,000 related taxa widely available. Inference of such large phylogenies takes hours or days using the Neighbour-Joining method on a normal desktop computer because of the O(n) running time. RapidNJ...

This paper explores a nearest neighbour pattern recognition method for time-series forecasting. A nearest neighbour method (FNNM) based on fuzzy membership values is developed. The main aim of the forecasting algorithm is to make single point forecasts into the future on the basis of past nearest neighbours. The nearest neighbours are selected using a membership threshold value. The results inc...

The phylogenetic position of Vibrio iliopiscarius was inferred by the maximum-likelihood, maximum-parsimony and neighbour-joining methods on the basis of almost complete 16S rRNA gene sequences. The results showed that this species falls into the same cluster as Photobacterium species and is clearly distinct from other Vibrio species. Its nearest phylogenetic neighbour is Photobacterium phospho...

Citation: Ani VA (2015) Feasibility analysis and simulation of a stand-alone photovoltaic energy system for electricity generation and environmental sustainability – equivalent to 650VA fuel-powered generator – popularly known as “I pass my neighbour”. Front. Energy Res. 3:38. doi: 10.3389/fenrg.2015.00038 Feasibility analysis and simulation of a stand-alone photovoltaic energy system for elect...

In this paper we describe the parallelization of two nearest neighbour classification algorithms. Nearest neighbour methods are well-known machine learning techniques. They have been successfully applied to Text Categorization task. Based on standard parallel techniques we propose two versions of each algorithm on message passing architectures. We also include experimental results on a cluster ...

We study self-avoiding and neighbour-avoiding walks and lattice trails on two semiregular lattices, the (3.122) lattice and the (4.82) lattice. For the (3.122) lattice we find the exact connective constant for both self-avoiding walks, neighbour-avoiding walks and trails. For the (4.82) lattice we generate long series which permit the accurate estimation of the connective constant for self-avoi...

The k Nearest Neighbour (kNN) method is a widely used technique which has found several applications in clustering and classification. In this paper, we focus on classification problems and we propose modifications of the nearest neighbour method that exploit information from the structure of a dataset. The results of our experiments using datasets from the UCI repository demonstrate that the c...

The Approximating and Eliminating Search Algorithm (AESA) and related AESA-based techniques are among the fastest methods for (k-)Nearest Neighbour(s) searching in general metric spaces. These techniques can be optimized for the (easier) (k-)Nearest Neighbour(s) classification problem. In particular, an optimized version of the AESA is proposed here which is shown to be significantly faster tha...

Spam mail classification and filtering is a commonly investigated problem, yet there has been little research into the application of nearest neighbour classifiers in this field. This paper examines the possibility of using a nearest neighbour algorithm for simple, word based spam mail classification. This approach is compared to a neural network, and decision-tree along with results published ...