The class of k Nearest Neighbor (kNN) queries in spatial networks is extensively studied in the context of numerous applications. In this paper, for the first time we study a generalized form of this problem, called the Time-Dependent k Nearest Neighbor problem (TD-kNN) with which edge-weights are time variable. All existing approaches for kNN search assume that the weight (e.g., travel-time) o...

The k Nearest Neighbor (kNN) join operation associates each data object in one data set with its k nearest neighbors from the same or a different data set. The kNN join on high-dimensional data (high-dimensional kNN join) is an especially expensive operation. Existing high-dimensional kNN join algorithms were designed for static data sets and therefore cannot handle updates efficiently. In this...

Four versions of a k-nearest neighbor algorithm with locally adaptive k are introduced and compared to the basic k-nearest neighbor algorithm (kNN). Locally adaptive kNN algorithms choose the value of k that should be used to classify a query by consulting the results of cross-validation computations in the local neighborhood of the query. Local kNN methods are shown to perform similar to kNN i...

In this study, we explore the utility of data structures that facilitate efficient nearest neighbor searches for application in multi-source forest attribute prediction. Our trials suggest that the kd-tree in combination with exact search algorithms can greatly reduce nearest neighbor search time. Further, given our trial data, we found that enormous gain in search time efficiency, afforded by ...

Random KNN (RKNN) is a novel generalization of traditional nearest-neighbor modeling. Random KNN consists of an ensemble of base k-nearest neighbor models, each constructed from a random subset of the input variables. A collection of r such base classifiers is combined to build the final Random KNN classifier. Since the base classifiers can be computed independently of one another, the overall ...