Optimization of distance formula in K-Nearest Neighbor method Article Swipe

PDF

Related Concepts

Euclidean distance k-nearest neighbors algorithm Euclidean geometry Mathematics Nearest neighbor search Pattern recognition (psychology) Minkowski distance Fixed-radius near neighbors Distance measures Distance measurement Artificial intelligence Large margin nearest neighbor Object (grammar) Euclidean distance matrix Value (mathematics) Best bin first Minimum distance Computer science Mathematical optimization Statistics Cluster analysis Geometry Canopy clustering algorithm Correlation clustering

Arif Ridho Lubis , Muharman Lubis , Al-Khowarizmi Al-Khowarizmi ·

YOU? · · 2020 · Open Access · · DOI: https://doi.org/10.11591/eei.v9i1.1464 · OA: W2990663423

K-Nearest Neighbor (KNN) is a method applied in classifying objects based on learning data that is closest to the object based on comparison between previous and current data. In the learning process, KNN calculates the distance of the nearest neighbor by applying the euclidean distance formula, while in other methods, optimization has been done on the distance formula by comparing it with the other similar in order to get optimal results. This study will discuss the calculation of the euclidean distance formula in KNN compared with the normalized euclidean distance, manhattan and normalized manhattan to achieve optimization results or optimal value in finding the distance of the nearest neighbor.