This study investigates the impact of data scaling techniques on performance k-nearest neighbor (KNN) algorithm using ten different datasets from various domains. Three commonly used techniques, min-max normalization, Z-score, and decimal scaling, are evaluated based KNN algorithm's in terms accuracy, precision, recall, F1-score, runtime, memory usage. The aims to provide insights into applicab...

A k-nearest neighbor search algorithm for the Pyramid technique is presented. The Pyramid technique divides d-dimensional data space into 2d pyramids. Given a query point q, we initialize the radius of a range query to be the furthest distance of the k candidate nearest neighbors from q in the pyramid which q is in, then examine the rest of the pyramids one by one. After one pyramid is checked,...

One of the most known and effective methods in supervised classification is the K-Nearest Neighbors classifier. Several approaches have been proposed to enhance its precision, with the Fuzzy K-Nearest Neighbors (Fuzzy-kNN) classifier being among the most successful ones. However, despite its good behavior, Fuzzy-kNN lacks of a method for properly defining several mechanisms regarding the repres...

We describe the implementation of a fast, dynamic, approximate, nearest-neighbor search algorithm that works well in fixed dimensions (d ≤ 5), based on sorting points coordinates in Morton (or z-) ordering. Our code scales well on multi-core/cpu shared memory systems. Our implementation is competitive with the best approximate nearest neighbor searching codes available on the web, especially fo...

The nearest neighbor (NN) classifier is well suited for generic object recognition. However, it requires storing the complete training data, and classification time is linear in the amount of data. There are several approaches to improve runtime and/or memory requirements of nearest neighbor methods: Thinning methods select and store only part of the training data for the classifier. Efficient ...