Using branch-grafted r-trees for spatial data mining

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3036, Issue , 2004, Pages 657-660

  Dubey, Priyanka ^a   Chen, Zhengxin ^a   Shi, Yong ^a,b  

^a UNIVERSITY OF NEBRASKA AT OMAHA   (United States)

^b UNIVERSITY OF CHINESE ACADEMY OF SCIENCES   (China)

Author keywords

[No Author keywords available]

Indexed keywords

DECISION TREES; FORESTRY; TREES (MATHEMATICS);

BOTTOM UP APPROACH; DATA ACCESS; IMPLICIT INFORMATIONS; R-TREES; SPATIAL DATA MINING; SPATIAL DATA STRUCTURE; SPATIAL FEATURES; WEATHER PATTERNS;

DATA MINING;

EID: 35048817924     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-24685-5_113     Document Type: Article

References (6)

1. B. Asato, Branch-Grafting Heuristic for R-tree Implementation, Working Paper, Department of Computer Science, University of Nebraska at Omaha, 1994.
2. A. Guttman, R-trees: A Dynamic Index Structure for Spatial Searching, Proc. ACM IGMOD, 47-57, 1984
3. M. Khanijo, Z. Chen and Q. Zhu, Spatial data access methods using branch-grafted R-trees, Advances in Database and Knowledge-Based Systems: Data Mining and Data Warehousing (G. E. Lasker and Z. Chen eds.), 31-36, 2000.
4. K. Koperski, J. Han, and J. Adhikary, Mining Knowledge in Geographical Data, IEEE Computer, 1998.
5. P. Dubey, Spatial Data Mining Using Branch-Grafted R-tree, MS thesis, University of Nebraska at Omaha, Omaha, 2003.
6. T. Schreck and Z. Chen, Branch grafting method for R-tree implementation, Journal of Systems and Software, 53(1), 83-93, 2000.