Vorono trees and clustering problems
Information Systems
Distance-based indexing for high-dimensional metric spaces
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
ACM Computing Surveys (CSUR)
Slim-Trees: High Performance Metric Trees Minimizing Overlap Between Nodes
EDBT '00 Proceedings of the 7th International Conference on Extending Database Technology: Advances in Database Technology
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
Near Neighbor Search in Large Metric Spaces
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Searching in Metric Spaces by Spatial Approximation
SPIRE '99 Proceedings of the String Processing and Information Retrieval Symposium & International Workshop on Groupware
Index-driven similarity search in metric spaces (Survey Article)
ACM Transactions on Database Systems (TODS)
A compact space decomposition for effective metric indexing
Pattern Recognition Letters
Similarity Search: The Metric Space Approach (Advances in Database Systems)
Similarity Search: The Metric Space Approach (Advances in Database Systems)
A Data Structure and an Algorithm for the Nearest Point Problem
IEEE Transactions on Software Engineering
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Clustering-based methods for searching in metric spaces partition the space into a set of disjoint clusters. When solving a query, some clusters are discarded without comparing them with the query object, and clusters that can not be discarded are searched exhaustively. In this paper we propose a new strategy and algorithms for clustering-based methods that avoid the exhaustive search within clusters that can not be discarded, at the cost of some extra information in the index. This new strategy is based on progressively reducing the cluster until it can be discarded from the result. We refer to this approach as cluster reduction. We present the algorithms for range and kNN search. The results obtained in an experimental evaluation with synthetic and real collections show that the search cost can be reduced by a 13% - 25% approximately with respect to existing methods.