Criteria that induce a Skyline naturally represent user’s preference conditions useful to discard irrelevant data in large datasets. However, in the presence of high-dimensional Skyline spaces, the size of the Skyline can still be very large, making unfeasible for users to process this set of points. To identify the best points among the Skyline, the Top-k Skyline approach has been proposed. To...

A k-nearest neighbor (kNN) query determines the k nearest points, using distance metrics, from a specific location. An all k-nearest neighbor (AkNN) query constitutes a variation of a kNN query and retrieves the k nearest points for each point inside a database. Their main usage resonates in spatial databases and they consist the backbone of many location-based applications and not only (i.e. k...

Advances in mobile technologies and devices has changed the way users interact with devices and other users. These new interaction methods and services are offered by the help of intelligent sensing capabilities, using context, location and motion sensors. However, indoor location sensing is mostly achieved by utilizing radio signal (Wi-Fi, Bluetooth, GSM etc.) and nearest neighbor identificati...

In mobile communication, spatial queries pose a serious threat to user location privacy because the location of a query may reveal sensitive information about the mobile user. In this paper, study approximate k nearest neighbor (kNN) queries where the mobile user queries the location-based service (LBS) provider about approximate k nearest points of interest (POIs) on the basis of his current l...

A novel clustering algorithm CSHARP is presented for the purpose of finding clusters of arbitrary shapes and arbitrary densities in high dimensional feature spaces. It can be considered as a variation of the Shared Nearest Neighbor algorithm (SNN), in which each sample data point votes for the points in its k-nearest neighborhood. Sets of points sharing a common mutual nearest neighbor are cons...

Our goal in this paper is to examine the application of Voronoi diagrams, a fundamental concept of computational geometry, to the nearest neighbor algorithm used in machine learning. We consider the question “Given a planar polygonal tessellation T and an integer k, is there a set of k points whose Voronoi diagram contains every edge in T?” We show that this question is NP-hard. We encountered ...

palmprint recognition is a new biometrics system based on physiological characteristics of the palmprint, which includes rich, stable, and unique features such as lines, points, and texture. texture is one of the most important features extracted from low resolution images. in this paper, a new local descriptor, local composition derivative pattern (lcdp) is proposed to extract smartly stronger...

The nearest neighbor search problem in general dimensions finds application in computational geometry, computational statistics, pattern recognition, and machine learning. Although there is a significant body of work on theory and algorithms, surprisingly little work has been done on algorithms for high-end computing platforms and no open source library exists that can scale efficiently to thou...

In this paper, we present algorithms for the top-k nearest neighbor searching where the input points are exact and the query point is uncertain under the L1 metric in the plane. The uncertain query point is represented by a discrete probability distribution function, and the goal is to efficiently return the top-k expected nearest neighbors, which have the smallest expected distances to the que...

We consider machine learning in a comparison-based setting where we are given a set of points in a metric space, but we have no access to the actual distances between the points. Instead, we can only ask an oracle whether the distance between two points i and j is smaller than the distance between the points i and k. We are concerned with data structures and algorithms to find nearest neighbors...