Title :
Similarity Search Problem Research on Multi-dimensional Data Sets
Author :
Yong Shi ; Graham, Brian
Author_Institution :
Dept. of Comput. Sci., Kennesaw State Univ., Kennesaw, GA, USA
Abstract :
In this paper, we present our continuous work on designing an algorithm to find nearest neighbors to given queries. In our previous work, we analyze the situation that there are multiple queries with different level of importance, and define a weight for each query point. We also propose an algorithm to find nearest neighbors to multiple queries with weights and enhanced our algorithm based on query point distribution. In this paper we analyze the data distribution on various dimensions, and apply the shrinking concept for the improvement and enhancement of our multi-query search approach.
Keywords :
data handling; query processing; search problems; data distribution; multidimensional data sets; multiple queries; multiquery search approach; nearest neighbors; query point distribution; similarity search problem research; Accuracy; Algorithm design and analysis; Data mining; Data preprocessing; Histograms; Nearest neighbor searches; Search problems; K Nearest Search; Multi-query; Similarity Search;
Conference_Titel :
Information Technology: New Generations (ITNG), 2013 Tenth International Conference on
Conference_Location :
Las Vegas, NV
Print_ISBN :
978-0-7695-4967-5
DOI :
10.1109/ITNG.2013.72