Paper Info
Title
wNeighbors: a method for finding k nearest neighbors in weighted regions
Abstract
As the fundamental application of Location Based Service (LBS), k nearest neighbors query has received dramatic attention. In this paper, for the first time, we study how to monitor the weighted k nearest neighbors(WkNN) in a novel weighted space to reflect more complex scenario. Different from traditional kNN approaches, the distances are measured according to a weighted Euclidean metric. The length of a path is defined to be the sum of its weighted subpaths, where a weighted subpath is relative to the weights of its passing regions. By dividing the plane into a set of Combination Regions, a data structure "Weighted Indexing Map"(WIM) is presented. The WIM keeps an index of the weighted length information. Based on WIM, we propose an efficient algorithm, called wNeighbors, for answering the WkNN query. The experimental results show that our WIM based WkNN processing algorithm are effective and efficient.
Year
DOI
Venue
2011
10.1007/978-3-642-20152-3_11
DASFAA (2)
Keywords
Field
DocType
weighted subpath,novel weighted space,weighted k,neighbors query,wknn processing algorithm,weighted region,efficient algorithm,wknn query,weighted length information,combination regions,weighted subpaths,knn,lbs
k-nearest neighbors algorithm,Data structure,Data mining,Pattern recognition,Division (mathematics),Computer science,Euclidean distance,Search engine indexing,Location-based service,Artificial intelligence,Weighted space
Conference
Volume
ISSN
Citations 
6588
0302-9743
3
PageRank 
References 
Authors
0.38
20
4
Name
Order
Citations
PageRank
Chuanwen Li1489.53
Yu Gu220134.98
Ge YU31313175.88
Fangfang Li4143.59