DocumentCode :
710284
Title :
An Attempt to Find Information for Multi-dimensional Data Sets
Author :
Yong Shi ; Sunpil Kim
Author_Institution :
Dept. of Comput. Sci., Kennesaw State Univ., Kennesaw, GA, USA
fYear :
2015
fDate :
13-15 April 2015
Firstpage :
763
Lastpage :
764
Abstract :
In this paper, we present our work on analyzing data sets that contain a large amount of data points. We study similarity search problems that find data points closest to a given query point. We also study cluster analysis that detects subgroups of data points from a data set that are similar to each other within the same subgroup. In this paper we design an algorithm to detect the clusters in subspaces that are readjusted continuously when the data set changes and new query requests come. The reconstructed clusters can help improve the performance of the future K nearest search process.
Keywords :
learning (artificial intelligence); query processing; set theory; k nearest search process; multidimensional data sets; query point; reconstructed clusters; Clustering algorithms; Data mining; Knowledge discovery; Nearest neighbor searches; Noise; Search problems; Spatial databases; K Nearest Search; Multi-query; Similarity Search;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Technology - New Generations (ITNG), 2015 12th International Conference on
Conference_Location :
Las Vegas, NV
Print_ISBN :
978-1-4799-8827-3
Type :
conf
DOI :
10.1109/ITNG.2015.134
Filename :
7113573
Link To Document :
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