Hierarchical clustering-merging for multidimensional index structures

Authors:
Zhan Chen;Jing Ding;Mu Zhang;Wallapak Tavanapong;Johnny S. Wong
Affiliations:
Department of Computer Science, Iowa State University, Ames, IA;Department of Computer Science, Iowa State University, Ames, IA;Department of Computer Science, Iowa State University, Ames, IA;Department of Computer Science, Iowa State University, Ames, IA;Department of Computer Science, Iowa State University, Ames, IA
Venue:
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
Year:
2003

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Abstract

The R-tree family index structures are among the most common index structures used in multidimensional databases. To improve the search performance it is very important to reduce the overlap between bounding regions in the R-tree. However the arbitrary insertion order in the tree construction procedure might result in tree structures inefficient in the search operations. In this paper we propose a new technique called Hierarchical Clustering-Merging (HCM) to improve the tree construction procedure of the R-tree family index structures. With this technique we can take advantage of the data distribution information in the data set to achieve an optimized tree structure and improve the search performance.