K-Nearest Neighbor (K-NN) algorithm is a classification that has been proven to solve various problems. Two approaches can be used in this are K-NN with Euclidean and Manhattan. The research aims apply the Manhattan classify accuracy of graduation. Student graduation determined by variables gender, major, number first-semester credits, second-semester third-semester grade point on first semeste...

We introduce a new nearest neighbor search algorithm. The algorithm builds a nearest neighbor graph in an offline phase and when queried with a new point, performs hill-climbing starting from a randomly sampled node of the graph. We provide theoretical guarantees for the accuracy and the computational complexity and empirically show the effectiveness of this algorithm.

Recently years, the nearest neighbor (N-N) query in the spatial database contracts much public attentions and is studied widely. Because of the kinds of the query objects, there are generally three types, point to point (p-p), point to segment (p-s) and segment to segment (s-s). Most of them are precise data, and related documents are rich. However, the research about querying fuzzy objects wit...

hStreams is a recently proposed (IPDPSW 2016) task-based target-agnostic heterogeneous streaming library that supports task concurrency over heterogeneous platforms. We share our experience of enabling a non-trivial machine learning (ML) algorithm: K-nearest neighbor using hStreams. The K-nearest neighbor (KNN) is a popular algorithm with numerous applications in machine learning, data-mining, ...

Cachaça is a type of distilled drink from sugarcane with great economic importance. Its classification includes three types: aged, premium and extra premium. These three classifications are related to the aging time of the drink in wooden casks. Besides the aging time, it is important to know what the wood used in the barrel storage in order the properties of each drink are properly informed co...

In this paper, a new classification method is presented which uses clustering techniques to augment the performance of K-Nearest Neighbor algorithm. This new method is called Nearest Cluster approach, NC. In this algorithm the neighbor samples are automatically determined using clustering techniques. After partitioning the train set, the labels of cluster centers are determined. For specifying ...

This paper presents a learning-based approach to segment postal address blocks where the learning step uses only one pair of images (a sample image and its ideal segmented solution). First, this approach learns the available knowledge among pixels (each gray level) in an input image and its corresponding output in the ideal segmented solution. A classification array is generated which is re-uti...

Place recognition addresses the problem of determining whether a robot is in a map, and if so, globally localizing, without being given any prior estimate. An efficient method of solving this problem involves selecting a set of keypoints which encode the local region, and then utilizing a sublinear-time nearest neighbors search into a database of keypoints previously generated from the global m...

The normal distribution is a usual one of various distributions in the real world. A normal neutrosophic set (NNS) is composed of both a normal fuzzy number and a neutrosophic number, which a significant tool for describing the incompleteness, indeterminacy, and inconsistency of the decision-making information. In this paper, we propose two correlation coefficients between NNSs based on the sco...

This paper explores a method of performing localization on local vision mobile robots. It describes a method of processing images, extracting regions, and comparing those regions against a database of preprocessed images. Localization is achieved using the k-nearest neighbour algorithm as the basis for approximating the current position. Initial results are provided that show the potential of t...