Abstract
A number of problems in computer science can be solved efficiently with the so called memory based or kernel methods. Among this problems (relevant to the AI community) are multimedia indexing, clustering, non supervised learning and recommendation systems. The common ground to this problems is satisfying proximity queries with an abstract metric database.
In this paper we introduce a new technique for making practical indexes for metric range queries. This technique improves existing algorithm based on pivots and signatures, and introduce a new data structure, the Fixed Queries Trie to speedup metric range queries. The result is an O(n) construction time index, with query complexity O(n α), α≤ 1. The indexing algorithm uses only a few bits of storage for each database element.
Partially supported by CONACyT grant R-36911A and CYTED VII.19 RIBIDI.
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Chávez, E., Figueroa, K. (2004). Faster Proximity Searching in Metric Data. In: Monroy, R., Arroyo-Figueroa, G., Sucar, L.E., Sossa, H. (eds) MICAI 2004: Advances in Artificial Intelligence. MICAI 2004. Lecture Notes in Computer Science(), vol 2972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24694-7_23
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DOI: https://doi.org/10.1007/978-3-540-24694-7_23
Publisher Name: Springer, Berlin, Heidelberg
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