k-Nearest Neighbour Classifiers: 2nd Edition (with Python examples)

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 because issues of poor run-time performance is not such a problem these days with the computational power that is available. This paper presents an overview of techniques for Nearest Neighbour classification focusing on; mechanisms for assessing similarity (distance), computational issues in identifying nearest neighbours and mechanisms for reducing the dimension of the data. This paper is the second edition of a paper previously published as a technical report. Sections on similarity measures for time-series, retrieval speed-up and intrinsic dimensionality have been added. An Appendix is included providing access to Python code for the key methods.Comment: 22 pages, 15 figures: An updated edition of an older tutorial on kN

Methods of Hierarchical Clustering

We survey agglomerative hierarchical clustering algorithms and discuss efficient implementations that are available in R and other software environments. We look at hierarchical self-organizing maps, and mixture models. We review grid-based clustering, focusing on hierarchical density-based approaches. Finally we describe a recently developed very efficient (linear time) hierarchical clustering algorithm, which can also be viewed as a hierarchical grid-based algorithm.Comment: 21 pages, 2 figures, 1 table, 69 reference

Property-based biomass feedstock grading using k-Nearest Neighbour technique

Abstract: Energy generation from biomass requires a nexus of different sources irrespective of origin. A detailed and scientific understanding of the class to which a biomass resource belongs is therefore highly essential for energy generation. An intelligent classification of biomass resources based on properties offers a high prospect in analytical, operational and strategic decision-making. This study proposes the -Nearest Neighbour (-NN) classification model to classify biomass based on their properties. The study scientifically classified 214 biomass dataset obtained from several articles published in reputable journals. Four different values of (=1,2,3,4) were experimented for various self normalizing distance functions and their results compared for effectiveness and efficiency in order to determine the optimal model. The -NN model based on Mahalanobis distance function revealed a great accuracy at =3 with Root Mean Squared Error (RMSE), Accuracy, Error, Sensitivity, Specificity, False positive rate, Kappa statistics and Computation time (in seconds) of 1.42, 0.703, 0.297, 0.580, 0.953, 0.047, 0.622, and 4.7 respectively. The authors concluded that -NN based classification model is feasible and reliable for biomass classification. The implementation of this classification models shows that -NN can serve as a handy tool for biomass resources classification irrespective of the sources and origins

k-Nearest Neighbour Classifiers - A Tutorial

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 because issues of poor run-time performance is not such a problem these days with the computational power that is available. This paper presents an overview of techniques for Nearest Neighbour classification focusing on; mechanisms for assessing similarity (distance), computational issues in identifying nearest neighbours and mechanisms for reducing the dimension of the data.This paper is the second edition of a paper previously published as a technical report . Sections on similarity measures for time-series, retrieval speed-up and intrinsic dimensionality have been added. An Appendix is included providing access to Python code for the key methods