An algorithm for finding nearest neighbours in (approximately) constant average time
Pattern Recognition Letters
Some approaches to best-match file searching
Communications of the ACM
ACM Computing Surveys (CSUR)
Fixed Queries Array: A Fast and Economical Data Structure for Proximity Searching
Multimedia Tools and Applications
Near Neighbor Search in Large Metric Spaces
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Index-driven similarity search in metric spaces (Survey Article)
ACM Transactions on Database Systems (TODS)
Foundations of Multidimensional and Metric Data Structures (The Morgan Kaufmann Series in Computer Graphics and Geometric Modeling)
A compact space decomposition for effective metric indexing
Pattern Recognition Letters
Solving similarity joins and range queries in metric spaces with the list of twin clusters
Journal of Discrete Algorithms
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One of the most efficient index for similarity search, to fix ideas think in speeding up k-nn searches in a very large database, is the so called list of clusters. This data structure is a counterintuitive construction which can be seen as extremely unbalanced, as opposed to balanced data structures for exact searching. In practical terms there is no better alternative for exact indexing, when every search return all the incumbent results; as opposed to approximate similarity search. The major drawback of the list of clusters is its quadratic time construction. In this paper we revisit the list of clusters aiming at speeding up the construction time without sacrificing its efficiency. We obtain similar search times while gaining a significant amount of time in the construction phase.