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In this paper we focus on the problem of continuously monitoring the set of Reverse k-Nearest Neighbors (RkNNs) of a query object in a moving object database using a client server...
In this paper, we propose a novel non-parametric clustering method based on non-parametric local shrinking. Each data point is transformed in such a way that it moves a specific ...
A new access method, called M-tree, is proposed to organize and search large data sets from a generic "metric space", i.e. where object proximity is only defined by a di...
: The reverse k-nearest neighbor (RkNN) problem, i.e. finding all objects in a data set the k-nearest neighbors of which include a specified query object, has received increasing a...
Classification based on k-nearest neighbors (kNN classification) is one of the most widely used classification methods. The number k of nearest neighbors used for achieving a high ...
In the typical nonparametric approach to classification in instance-based learning and data mining, random data (the training set of patterns) are collected and used to design a d...
Binay K. Bhattacharya, Kaustav Mukherjee, Godfried...
In this paper, we formalize the novel concept of Constrained Reverse k-Nearest Neighbor (CRkNN) search on mobile objects (clients) performed at a central server. The CRkNN query c...
In Handwritten Character Recognition, the rejection of extraneous patterns, like image noise, strokes or corrections, can improve significantly the practical usefulness of a syste...
Javier Cano, Joaquim Arlandis, Juan Carlos P&eacut...
An empirical study of the domain of patch-based learning algorithms for image and video processing is conducted. As patch-based algorithms are commonly used, knowledge of the prop...