Computational geometry: an introduction
Computational geometry: an introduction
Vector quantization and signal compression
Vector quantization and signal compression
Equal-average hyperplane partitioning method for vector quantization of image data
Pattern Recognition Letters
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
A cost model for nearest neighbor search in high-dimensional data space
PODS '97 Proceedings of the sixteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
The TV-tree: an index structure for high-dimensional data
The VLDB Journal — The International Journal on Very Large Data Bases - Spatial Database Systems
Studies in computational geometry motivated by mesh generation
Studies in computational geometry motivated by mesh generation
An Algorithm for Finding Nearest Neighbors
IEEE Transactions on Computers
On building graphs of documents with artificial ants
Proceedings of the 16th international conference on World Wide Web
Incremental Neighborhood Graphs Construction for Multidimensional Databases Indexing
CAI '07 Proceedings of the 20th conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
HRG: A Graph Structure for Fast Similarity Search in Metric Spaces
DEXA '08 Proceedings of the 19th international conference on Database and Expert Systems Applications
Similarity and Kernel Matrix Evaluation Based on Spatial Autocorrelation Analysis
ISMIS '09 Proceedings of the 18th International Symposium on Foundations of Intelligent Systems
Neighborhood graphs for indexing and retrieving multi-dimensional data
Journal of Intelligent Information Systems
Neighborhood graphs for semi-automatic annotation of large image databases
MMM'07 Proceedings of the 13th international conference on Multimedia Modeling - Volume Part I
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Neighborhood graphs are an effective and very widespread technique in several fields. But, in spite of the neighborhood graphs interest, their construction algorithms suffer from a very high complexity what prevents their implementation for great data volumes processing applications. With this high complexity, the update task is also affected. These structures constitute actually a possible representation of the point location problem in a multidimensional space. The point location on an axis can be solved by a binary research. This same problem in the plan can be solved by using a voronoi diagram, but when dimension becomes higher, the location becomes more complex and difficult to manage. We propose in this paper an effective method for point location in a multidimensional space with an aim of effectively and quickly updating neighborhood graphs.