The two-dimensional hexagonal grid and the three-dimensional face-centered cubic grid can be described by intersecting Z and Z with a (hyper)plane. Corresponding grids in higher dimensions (nD) are examined. In this paper, we define distance functions based on neighborhood sequences on these, higher dimensional generalizations of the hexagonal grid. An algorithm to produce a shortest path based...

Nearest neighbor is pattern matching method for time series prediction in which most recent values of the time series are compared with previous available values and forecasting is achieved by finding the best match pattern (nearest neighbor). Usually Euclidean distance is used to check the similarity of pattern. In this paper two hybrid criteria of pattern matching are being proposed and evalu...

To classify time series by nearest neighbor, we need to specify or learn a distance. We consider several variations of the Mahalanobis distance and the related Large Margin Nearest Neighbor Classification (LMNN). We find that the conventional Mahalanobis distance is counterproductive. However, both LMNN and the class-based diagonal Mahalanobis distance are competitive.

Urban hotspot area detection is an important issue that needs to be explored for urban planning and traffic management. It of great significance mine hotspots from taxi trajectory data, which reflect residents’ travel characteristics the operational status traffic. The existing clustering methods mainly concentrate on number objects contained in within a specified size, neglecting impact local ...

Clustering is one of the better known unsupervised learning methods with the aim of discovering structures in the data. This paper presents a distance-based Sweep-Hyperplane Clustering Algorithm (SHCA), which uses sweep-hyperplanes to quickly locate each point’s approximate nearest neighbourhood. Furthermore, a new distance-based dynamic model that is based on 2N -tree hierarchical space partit...

We discuss the use of online learning of the local search neighborhood. Specifically, we consider the Linkage Tree Genetic Algorithm (LTGA), a population-based, stochastic local search algorithm that learns the neighborhood by identifying the problem variables that have a high mutual information in a population of good solutions. The LTGA builds each generation a linkage tree using a hierarchic...

Unlike traditional classification tasks, multilabel classification allows a sample to associate with more than one label. This generalization naturally arises the difficulty in classification. Similar to the single label classification task, neighborhood-based algorithms relying on the nearest neighbor have attracted lots of attention and some of them show positive results. In this paper, we pr...

Obesity in the United States does not affect all segments of the population equally. It is more prevalent in deprived neighborhoods and among groups with lower education and incomes. Inequitable access to healthy foods is one mechanism by which socioeconomic factors can influence food choice behaviors, overall diet quality, and bodyweight. Having a supermarket in the immediate neighborhood has ...

This paper presents a new hybrid classifier that combines the Nearest Neighbor distance based algorithm with the Classification Tree paradigm. The Nearest Neighbor algorithm is used as a preprocessing algorithm in order to obtain a modified training database for the posterior learning of the classification tree structure; experimental section shows the results obtained by the new algorithm; com...

OBJECTIVE There has been limited study of all types of food stores, such as traditional (supercenters, supermarkets, and grocery stores), convenience stores, and non-traditional (dollar stores, mass merchandisers, and pharmacies) as potential opportunities for purchase of fresh and processed (canned and frozen) fruits and vegetables, especially in small-town or rural areas. METHODS Data from ...