Title :
An Attempt to Find Neighbors
Author :
Shi, Yong ; Rosenblum, Ryan
Author_Institution :
Dept. of Comput. Sci. & Inf. Syst., Kennesaw State Univ., Kennesaw, GA, USA
Abstract :
In this paper, we present our continuous research on similarity search problems. Previously we proposed PanKNN[18] which is a novel technique that explores the meaning of K nearest neighbors from a new perspective, redefines the distances between data points and a given query point Q, and efficiently and effectively selects data points which are closest to Q. It can be applied in various data mining fields. In this paper, we present our approach to solving the similarity search problem in the presence of obstacles. We apply the concept of obstacle points and process the similarity search problems in a different way. This approach can assist to improve the performance of existing data analysis approaches.
Keywords :
data analysis; search problems; K nearest neighbors; data analysis; obstacle points; similarity search problems; Algorithm design and analysis; Clustering algorithms; Data mining; Measurement; Nearest neighbor searches; Search problems; USA Councils; Fuzzy; K nearest neighbors; data mining; query; similarity;
Conference_Titel :
Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), 2010 International Conference on
Conference_Location :
Huangshan
Print_ISBN :
978-1-4244-8434-8
Electronic_ISBN :
978-0-7695-4235-5
DOI :
10.1109/CyberC.2010.64