"Simulation of Extreme Dry and Wet Spells in Brahmaputra Basin Using K-Nearest Neighbour Model"

k-Nearest Neighbour Classifiers

Perhaps the most straightforward classifier in the arsenal or machine learning techniques is the Nearest Neighbour Classifier – classification is achieved by identifying the nearest neighbours to a query example and using those neighbours to determine the class of the query. This approach to classification is of particular importance today because issues of poor run-time performance is not such...

A synoptic study on seasonal patterns of wet and dry spells in midwest of Iran

The effective patterns and mechanisms of synoptic systems during the wet and dry spells in Midwest of Iran(MWI) analyzed and discussed on seasonal scales from 1974 to 2003. Synoptic Analysis is based on synoptic chartson Sea Level Pressure (SLP), 850 and 500 Hpa levels. The results of synoptic analysis show that dry spells in MWIare mostly corresponded to Azores High (AZH) intensifying and its ...

Coevolving Functions in Genetic Programming: Classification using K-nearest-neighbour

Small components in k-nearest neighbour graphs

Let G = Gn,k denote the graph formed by placing points in a square of area n according to a Poisson process of density 1 and joining each point to its k nearest neighbours. In [2] Balister, Bollobás, Sarkar and Walters proved that if k < 0.3043 logn then the probability that G is connected tends to 0, whereas if k > 0.5139 logn then the probability that G is connected tends to 1. We prove that,...

Convergence of random k-nearest-neighbour imputation

Random k-nearest-neighbour (RKNN) imputation is an established algorithm for filling in missing values in data sets. Assume that data are missing in a random way, so that missingness is independent of unobserved values (MAR), and assume there is a minimum positive probability of a response vector being complete. Then RKNN, with k equal to the square root of the sample size, asymptotically produ...