Computational geometry: an introduction
Computational geometry: an introduction
The LSD tree: spatial access to multidimensional and non-point objects
VLDB '89 Proceedings of the 15th international conference on Very large data bases
The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
A review of recent work on multi-attribute access methods
ACM SIGMOD Record
Equal-average hyperplane partitioning method for vector quantization of image data
Pattern Recognition Letters
The SR-tree: an index structure for high-dimensional nearest neighbor queries
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
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
Multidimensional access methods
ACM Computing Surveys (CSUR)
Self-Organizing Maps and Learning Vector Quantization forFeature Sequences
Neural Processing Letters
The Quadtree and Related Hierarchical Data Structures
ACM Computing Surveys (CSUR)
Multidimensional binary search trees used for associative searching
Communications of the ACM
ACM Computing Surveys (CSUR)
Modern Information Retrieval
The K-D-B-tree: a search structure for large multidimensional dynamic indexes
SIGMOD '81 Proceedings of the 1981 ACM SIGMOD international conference on Management of data
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
A Robust Multi-Attribute Search Structure
Proceedings of the Fifth International Conference on Data Engineering
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
Neighborhood graphs for semi-automatic annotation of large image databases
MMM'07 Proceedings of the 13th international conference on Multimedia Modeling - Volume Part I
An effective method for locally neighborhood graphs updating
DEXA'05 Proceedings of the 16th international conference on Database and Expert Systems Applications
Analogy-based reasoning in classifier construction
Transactions on Rough Sets IV
Neighborhood systems and approximate retrieval
Information Sciences: an International Journal
Fast approximate similarity search based on degree-reduced neighborhood graphs
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
A graph model for mutual information based clustering
Journal of Intelligent Information Systems
A re-coloring approach for graph b-coloring based clustering
International Journal of Knowledge-based and Intelligent Engineering Systems
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We propose a methodology based on a structure called neighborhood graphs for indexing and retrieving multi-dimensional data. In accordance with the increase of the quantity of data, it gets more and more important to process multi-dimensional data. Processing of data includes various tasks, for instance, mining, classifying, clustering, to name a few. However, to enable the effective processing of such multi-dimensional data, it is often necessary to locate each data precisely in the multi-dimensional space where the data reside so that each data can be effectively retrieved for processing. This amounts to solving the point location problem (neighborhood search) for multi-dimensional space. In this paper, in order to utilize the structure of neighborhood graphs as an indexing structure for multi-dimensional data, we propose the following: i) a local insertion and deletion method, and ii) an incremental neighborhood graph construction method. The first method enables to cope with the problem incurred from the updating of the graph. The second method realizes fast neighborhood graph construction from scratch, through the recursive application of the first method. Several experiments are conducted to evaluate the proposed approach, and the results indicate the effectiveness of our approach.