Spatial clustering based on moving distance in the presence of obstacles

7Citations
Citations of this article
3Readers
Mendeley users who have this article in their library.
Get full text

Abstract

The previous spatial clustering methods calculate the distance value between two spatial objects using the Euclidean distance function, which cannot reflect the grid path, and their computational complexity is high in the presence of obstacles. Therefore, in this paper, we propose a novel spatial clustering algorithm called DBSCAN-MDO. It reflects the grid path in the real world using the Manhattan distance function and reduces the number of obstacles to be considered by grouping obstacles in accordance with MBR of each cluster and filtering obstacles that do not affect the similarity between spatial objects. © Springer-Verlag Berlin Heidelberg 2007.

References Powered by Scopus

AUTOCLUSt+: Automatic clustering of point-data sets in the presence of obstacles

49Citations
N/AReaders
Get full text

Clustering spatial data in the presence of obstacles: A density-based approach

36Citations
N/AReaders
Get full text

Cited by Powered by Scopus

Continuous obstructed nearest neighbor queries in spatial databases

56Citations
N/AReaders
Get full text

Continuous visible nearest neighbor query processing in spatial databases

45Citations
N/AReaders
Get full text

Continuous nearest-neighbor search in the presence of obstacles

22Citations
N/AReaders
Get full text

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Cite

CITATION STYLE

APA

Park, S. H., Lee, J. H., & Kim, D. H. (2007). Spatial clustering based on moving distance in the presence of obstacles. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4443 LNCS, pp. 1024–1027). Springer Verlag. https://doi.org/10.1007/978-3-540-71703-4_96

Readers' Seniority

Tooltip

Professor / Associate Prof. 2

67%

PhD / Post grad / Masters / Doc 1

33%

Readers' Discipline

Tooltip

Computer Science 1

33%

Engineering 1

33%

Earth and Planetary Sciences 1

33%

Save time finding and organizing research with Mendeley

Sign up for free