Communications of the ACM - Special issue on parallelism
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 Nearest Hyperrectangle Learning Method
Machine Learning
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Unifying instance-based and rule-based induction
Machine Learning
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
Artificial Intelligence Review - Special issue on lazy learning
Data structures and algorithms for nearest neighbor search in general metric spaces
SODA '93 Proceedings of the fourth annual ACM-SIAM Symposium on Discrete algorithms
The Grid File: An Adaptable, Symmetric Multikey File Structure
ACM Transactions on Database Systems (TODS)
Multidimensional binary search trees used for associative searching
Communications of the ACM
Planning and Learning by Analogical Reasoning
Planning and Learning by Analogical Reasoning
Machine Learning
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
The TV-tree: an index structure for high-dimensional data
The VLDB Journal — The International Journal on Very Large Data Bases - Spatial Database Systems
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Similarity Indexing with the SS-tree
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
When Is ''Nearest Neighbor'' Meaningful?
ICDT '99 Proceedings of the 7th International Conference on Database Theory
On the Surprising Behavior of Distance Metrics in High Dimensional Spaces
ICDT '01 Proceedings of the 8th International Conference on Database Theory
M-tree: An Efficient Access Method for Similarity Search in Metric Spaces
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
The R+-Tree: A Dynamic Index for Multi-Dimensional Objects
VLDB '87 Proceedings of the 13th International Conference on Very Large Data Bases
Near Neighbor Search in Large Metric Spaces
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
The X-tree: An Index Structure for High-Dimensional Data
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
A study of distance-based machine learning algorithms
A study of distance-based machine learning algorithms
Center-Based Indexing for Nearest Neighbors Search
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
A Branch and Bound Algorithm for Computing k-Nearest Neighbors
IEEE Transactions on Computers
A Data Structure and an Algorithm for the Nearest Point Problem
IEEE Transactions on Software Engineering
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
RIONA: A New Classification System Combining Rule Induction and Instance-Based Learning
Fundamenta Informaticae
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The paper addresses the problem of indexing data for k nearest neighbors (k-nn) search. Given a collection of data objects and a similarity measure the searching goal is to find quickly the k most similar objects to a given query object. We present a top-down indexing method that employs a widely used scheme of indexing algorithms. It starts with the whole set of objects at the root of an indexing tree and iteratively splits data at each level of indexing hierarchy. In the paper two different data models are considered. In the first, objects are represented by vectors from a multi-dimensional vector space. The second, more general, is based on an assumption that objects satisfy only the axioms of a metric space. We propose an iterative k-means algorithm for tree node splitting in case of a vector space and an iterative k-approximate-centers algorithm in case when only a metric space is provided. The experiments show that the iterative k-means splitting procedure accelerates significantly k-nn searching over the one-step procedure used in other indexing structures such as GNAT, SS-tree and M-tree and that the relevant representation of a tree node is an important issue for the performance of the search process. We also combine different search pruning criteria used in BST, GHT nad GNAT structures into one and show that such a combination outperforms significantly each single pruning criterion. The experiments are performed for benchmark data sets of the size up to several hundreds of thousands of objects. The indexing tree with the k-means splitting procedure and the combined search criteria is particularly effective for the largest tested data sets for which this tree accelerates searching up to several thousands times.