Results 211 to 220 of about 88,733 (260)

K-Nearest Neighbor Finding Using MaxNearestDist

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008
Similarity searching often reduces to finding the k nearest neighbors to a query object. Finding the k nearest neighbors is achieved by applying either a depth- first or a best-first algorithm to the search hierarchy containing the data. These algorithms are generally applicable to any index based on hierarchical clustering.
Hanan Samet, H Samet
exaly   +3 more sources

Weighted K-Nearest Neighbor revisited

2016 23rd International Conference on Pattern Recognition (ICPR), 2016
In this paper we show that weighted K-Nearest Neighbor, a variation of the classic K-Nearest Neighbor, can be reinterpreted from a classifier combining perspective, specifically as a fixed combiner rule, the sum rule. Subsequently, we experimentally demonstrate that it can be rather beneficial to consider other combining schemes as well. In particular,
BICEGO, Manuele, Loog, M.
openaire   +3 more sources

A fuzzy K-nearest neighbor algorithm

IEEE Transactions on Systems, Man, and Cybernetics, 1985
Classification of objects is an important area of research and application in a variety of fields. In the presence of full knowledge of the underlying probabilities, Bayes decision theory gives optimal error rates. In those cases where this information is not present, many algorithms make use of distance or similarity among samples as a means of ...
James M. Keller, Michael R. Gray, James A. Givens   +2 more
openaire   +1 more source

K-Nearest Neighbors

2020
Please download the sample Excel files from https://github.com/hhohho/Learn-Data-Mining-through-Excel for this chapter’s exercises.
openaire   +1 more source

On reverse-k-nearest-neighbor joins

GeoInformatica, 2014
A reverse k-nearest neighbour (RkNN) query determines the objects from a database that have the query as one of their k-nearest neighbors. Processing such a query has received plenty of attention in research. However, the effect of running multiple RkNN queries at once (join) or within a short time interval (bulk/group query) has only received little ...
Tobias Emrich, Hans-Peter Kriegel, Peer Kröger, Johannes Niedermayer, Matthias Renz, Andreas Züfle   +5 more
openaire   +2 more sources

Multiple k nearest neighbor search

World Wide Web, 2016
The problem of kNN (k Nearest Neighbor) queries has received considerable attention in the database and information retrieval communities. Given a dataset D and a kNN query q, the k nearest neighbor algorithm finds the closest k data points to q. The applications of kNN queries are board, not only in spatio-temporal databases but also in many areas ...
Yu-Chi Chung, I-Fang Su, Chiang Lee, Pei-Chi Liu   +3 more
openaire   +1 more source

An adaptive k-nearest neighbor algorithm

2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery, 2010
An adaptive k-nearest neighbor algorithm (AdaNN) is brought forward in this paper to overcome the limitation of the traditional k-nearest neighbor algorithm (kNN) which usually identifies the same number of nearest neighbors for each test example. It is known that the value of k has crucial influence on the performance of the kNN algorithm, and our ...
Shiliang Sun, Rongqing Huang
openaire   +1 more source

K-Nearest Neighbors

2023
  +6 more sources

Hausdorff Distance with k-Nearest Neighbors

2012
Hausdorff distance (HD) is an useful measurement to determine the extent to which one shape is similar to another, which is one of the most important problems in pattern recognition, computer vision and image analysis. Howeverm, HD is sensitive to outliers. Many researchers proposed modifications of HD.
Jun Wang 0002, Ying Tan 0002
openaire   +2 more sources

Classification with learning k-nearest neighbors

Proceedings of International Conference on Neural Networks (ICNN'96), 2002
The nearest neighbor (NN) classifiers, especially the k-NN algorithm, are among the simplest and yet most efficient classification rules and are widely used in practice. We introduce three adaptation rules that can be used in iterative training of a k-NN classifier.
Jorma Laaksonen, Erkki Oja
openaire   +2 more sources