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Fastmap: A fast algorithm for indexing, data-mining and visualization of traditional and multimedia datasets (1995)
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BibTeX
@INPROCEEDINGS{Faloutsos95fastmap:a,
author = {Christos Faloutsos and King-Ip (David) Lin},
title = {Fastmap: A fast algorithm for indexing, data-mining and visualization of traditional and multimedia datasets},
booktitle = {},
year = {1995},
pages = {163--174},
publisher = {}
}
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Abstract
A very promising idea for fast searching in traditional and multimedia databases is to map objects into points in k-d space, using k feature-extraction functions, provided by a domain expert [Jag91]. Thus, we can subsequently use highly fine-tuned spatial access methods (SAMs), to answer several types of queries, including the `Query By Example' type (which translates to a range query); the `all pairs' query (which translates to a spatial join [BKSS94]); the nearest-neighbor or best-match query, etc. However, designing feature extraction functions can be hard. It is relatively easier for a domain expert to assess the similarity/distance of two objects. Given only the distance information though, it is not obvious how to map objects into points. This is exactly the topic of this paper. We describe a fast algorithm to map objects into points in some k-dimensional space (k is user-defined), such that the dis-similarities are preserved. There are two benefits from this mapping: ...
Keyphrases
fast algorithm fine-tuned spatial access method fast searching k-d space k-dimensional space spatial join bkss94 range query promising idea feature-extraction function several type domain expert jag91 similarity distance domain expert distance information feature extraction function best-match query multimedia database
