Spatial diffusion processes can be seen in many geographic phenomena that spread or migrate across space and over time. Studies of these processes were mostly done with verbal description until Hägerstrand (1966) started to approach it with quantitative models. A variety of attempts were made to continue this effort, but only with various degrees of success. Recognizing the critical role that d...

A data object is broad if it is one of the k-Nearest Neighbors (k-NN) of many data objects. We introduce a new database primitive called Generalized Nearest Neighbor (GNN) to express data broadness. We also develop three strategies to answer GNN queries efficiently for large datasets of multidimensional objects. The R*-Tree based search algorithm generates candidate pages and ranks them based o...

In discrete k-center and k-median clustering, we are given a set of points P in a metric space M , and the task is to output a set C ⊆ P, |C| = k, such that the cost of clustering P using C is as small as possible. For k-center, the cost is the furthest a point has to travel to its nearest center, whereas for k-median, the cost is the sum of all point to nearest center distances. In the fault-t...

In this paper, we present our research on data analysis and nearest neighbor search problems. A nearest neighbor search problem is normally described as finding data point or data points from a data set that are closest to a given query point. It is used in many research and industrial fields. In our paper, we propose an approach that explores the meaning of K nearest neighbors from a new persp...

A k-nearest neighbor (kNN) query, which retrieves nearest k points from a database is one of the fundamental query types in spatial databases. An all k-nearest neighbor query (AkNN query), a variation of a kNN query, determines the k-nearest neighbors for each point in the dataset in a query process. In this paper, we propose a method for processing AkNN queries in Hadoop. We decompose the give...

We suggest a simple modification to the Kd-tree search algorithm for nearest neighbor search resulting in an improved performance. The Kd-tree data structure seems to work well in finding nearest neighbors in low dimensions but its performance degrades even if the number of dimensions increases to more than two. Since the exact nearest neighbor search problem suffers from the curse of dimension...

collection of appropriate qualitative and quantitative data is necessary for proper management and planning. used the suitable inventory methods is necessary and accuracy of sampling methods dependent the inventory net and number of sample point. nearest neighbor sampling method is a one of distance methods and calculated by three equations (byth and riple, 1980; cotam and curtis, 1956 and cota...

We test the performance of the Monte Carlo renormalization method in the context of the Ising model on a triangular lattice. We apply a block-spin transformation which allows for an adjustable parameter so that the transformation can be optimized. This optimization purportedly brings the fixed point of the transformation to a location where the corrections to scaling vanish. To this purpose we ...

Let A be a nonempty closed subset (resp. nonempty bounded closed subset) of a metric space (X, d) and x ∈ X \ A. The nearest point problem (resp. the farthest point problem) w.r.t. x considered here is to find a point a0 ∈ A such that d(x, a0) = inf{d(x, a) : a ∈ A} (resp. d(x, a0) = sup{d(x, a) : a ∈ A}). We study the well posedness of nearest point problems and farthest point problems in geod...