Computational geometry: an introduction
Computational geometry: an introduction
A retrieval technique for similar shapes
SIGMOD '91 Proceedings of the 1991 ACM SIGMOD international conference on Management of data
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Efficient and effective querying by image content
Journal of Intelligent Information Systems - Special issue: advances in visual information management systems
Multidimensional access methods
ACM Computing Surveys (CSUR)
The onion technique: indexing for linear optimization queries
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
A New Convex Hull Algorithm for Planar Sets
ACM Transactions on Mathematical Software (TOMS)
An optimal real-time algorithm for planar convex hulls
Communications of the ACM
Determining the minimum-area encasing rectangle for an arbitrary closed curve
Communications of the ACM
Machine Learning
Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications
Data Mining and Knowledge Discovery
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
A Multistep Approach for Shape Similarity Search in Image Databases
IEEE Transactions on Knowledge and Data Engineering
Improving the Query Performance of High-Dimensional Index Structures by Bulk-Load Operations
EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
Algorithms for Mining Distance-Based Outliers in Large Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Fast Nearest Neighbor Search in Medical Image Databases
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Efficient User-Adaptable Similarity Search in Large Multimedia Databases
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Computational geometry.
Geometric transforms for fast geometric algorithms
Geometric transforms for fast geometric algorithms
Answering linear optimization queries with an approximate stream index
Knowledge and Information Systems
Real-time fuzzy switching regression analysis: a convex hull approach
Proceedings of the 11th International Conference on Information Integration and Web-based Applications & Services
FAW'10 Proceedings of the 4th international conference on Frontiers in algorithmics
Flexible aggregate similarity search
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Largest area convex hull of axis-aligned squares based on imprecise data
COCOON'11 Proceedings of the 17th annual international conference on Computing and combinatorics
Approaching the efficient frontier: cooperative database retrieval using high-dimensional skylines
DASFAA'05 Proceedings of the 10th international conference on Database Systems for Advanced Applications
SSDBM'12 Proceedings of the 24th international conference on Scientific and Statistical Database Management
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Determining the convex hull of a point set is a basic operation for many applications of pattern recognition, image processing, statistics, and data mining. Although the corresponding point sets are often large, the convex hull operation has not been considered much in a database context, and state-of-the-art algorithms do not scale well to non main-memory resident data sets. In this paper, we propose two convex hull algorithms which are based on multidimensional index structures such as R-trees. One of them traverses the index depth-first. The other algorithm assigns a priority to each active node (nodes which are not yet accessed but known to the system), which corresponds to the maximum distance of the node region to the tentative convex hull. We show both theoretically as well as experimentally that our algorithms outperform competitive techniques that do not exploit indexes.