ProbabilisticK-nearest neighbour (PKNN) classification has been introduced to improve the performance of the original K-nearest neighbour (KNN) classification algorithm by explicitly modelling uncertainty in the classification of each feature vector. However, an issue common to both KNN and PKNN is to select the optimal number of neighbours, K. The contribution of this paper is to incorporate t...

Five classifiers including the K-means, Fuzzy c-means, K-nearest neighbour, Multi-Layer Perceptron Neural Network and Probabilistic Neural Network classifiers are compared for application to colour grade classification and detection of bruising of Granny Smith apples. A number of suitable discriminate features are determined heuristically for the categorisation of four classes including: high g...

Existing methods for retrieving k-nearest neighbours suffer from the curse of dimensionality. We argue this is caused in part by inherent deficiencies of space partitioning, which is the underlying strategy used by almost all existing methods. We devise a new strategy that avoids partitioning the vector space and present a novel randomized algorithm that runs in time linear in dimensionality an...

A simpliied k nearest neighbour (knn) search for the R-tree 6] family is proposed in this paper. This method is modiied from the technique developed by Roussopoulos et al. 10]. The main approach aims to eliminate redundant searches when the data is highly correlated. We also describe how MINMAXDIST calculations can be avoided using MINDIST as the only distance metric which gives a signiicant sp...

k-nearest neighbour algorithm is a non-parametric machine learning algorithm generally used for classification. It is also known as instance based learning or lazy learning. K-NN algorithm can also be adapted for regression that is for estimating continuous variables. In this research paper the researcher endow with a generalized K-nearest algorithm used for predicting a continuous value. In or...