The K-nearest neighbor (KNN) decision rule has been a ubiquitous classification tool with good scalability. Past experience has shown that the optimal choice of K depends upon the data, making it laborious to tune the parameter for different applications. We introduce a new metric that measures the informativeness of objects to be classified. When applied as a query-based distance metric to mea...

Classification of spatial data streams is crucial, since the training dataset changes often. Building a new classifier each time can be very costly with most techniques. In this situation, k-nearest neighbor (KNN) classification is a very good choice, since no residual classifier needs to be built ahead of time. KNN is extremely simple to implement and lends itself to a wide variety of variatio...

Little work has been reported in the literature to support k-nearest neighbor (k-NN) searches/ queries in hybrid data spaces (HDS). An HDS is composed of a combination of continuous and non-ordered discrete dimensions. This combination presents new challenges in data organization and search ordering. In this paper, we present an algorithm for k-NN searches using a stages and use the properties ...

Let P be a Poisson process of intensity one in R2. For a fixed integer k, join every point of P to its k nearest neighbors, creating a directed random geometric graph ~ Gk(R). We prove bounds on the values of k that, almost surely, result in an infinite connected component in ~ Gk(R) for various definitions of “component”. We also give high confidence results for the exact values of k needed. I...

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...

For the last few years, a extensive research has been going on query processing of relation data and more practical and theoretical solution have been suggested to query processing under different scenarios. Now days cloud computing technology is increasing rapidly, so users now have the chance to store their data in remote location. However, different privacy issues are raised on cloud computi...

Let P be a set of n points in the plane. A geometric proximity graph on P is a graph where two points are connected by a straight-line segment if they satisfy some prescribed proximity rule. We consider four classes of higher order proximity graphs, namely, the k-nearest neighbor graph, the k-relative neighborhood graph, the k-Gabriel graph and the k-Delaunay graph. For k = 0 (k = 1 in the case...

Nearest neighbor-based techniques provide an approach to acoustic modeling that avoids the often lengthy and heuristic process of training traditional Gaussian mixturebased models. Here we study the problem of choosing the distance metric for a k-nearest neighbor (k-NN) phonetic frame classifier. We compare the standard Euclidean distance to two learned Mahalanobis distances, based on large-mar...

One of the difficulties that arises when using the K-nearest neighbor rule is that each of the labeled training samples is given equal importance in deciding the class of the query pattern to be classified, regardless of their typicality. In this paper, the theory of belief functions is introduced into the K-nearest neighbor rule to develop an evidential editing version of this algorithm. An ev...

This paper considers how to conduct k-nearest neighbor classification in the following scenario: multiple parties, each having a private data set, want to collaboratively build a k-nearest neighbor classifier without disclosing their private data to each other or any other parties. Specifically, the data are vertically partitioned in that all parties have data about all the instances involved, ...