Fast Map: A Fast Algorithms for Indexing, Data-Mining and Visualization of Traditional and Multimedia Datasets

dc.contributor.authorFaloutsos, Christosen_US
dc.contributor.authorLin, King-Ip D.en_US
dc.contributor.departmentISRen_US
dc.date.accessioned2007-05-23T09:57:16Z
dc.date.available2007-05-23T09:57:16Z
dc.date.issued1994en_US
dc.description.abstractA 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 uery By Example' type (which translates to a range query); the ll pairs' query (which translates to a spatial join [BKSS94]); the nearest-neighbor or best match query, etc.<P>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.<P>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: (a) efficient retrieval, in conjunction with a SAM, ad discussed before and (b) visualization and data-mining: the objects can now be plotted as points in 2-d or 3-d space, revealing potential clusters, correlations among attributes and other regularities that data-mining is looking for.<P>We introduce an older method from pattern recognition, namely, multi-Dimentional Scaling (MIDS) [Tor52]; although unsuitable for indexing, we use it as yardstick for our method. Then, we propose a much faster algorithm to solve the problem in hand, while in addition it allows for indexing. Experiments on real and synthetic data indeed show that the proposed algorithm is significantly faster than MIDS, (being linear, as opposed to quadratic, on the database size N), while it manages to preserve distances and the overall structure of the data-set.en_US
dc.format.extent1304823 bytes
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/1903/5550
dc.language.isoen_USen_US
dc.relation.ispartofseriesISR; TR 1994-80en_US
dc.subjectrobust information processingen_US
dc.subjectalgorithmsen_US
dc.subjectknowledge representationen_US
dc.subjectclusteringen_US
dc.subjectdatabasesen_US
dc.subjectSystems Integration Methodologyen_US
dc.titleFast Map: A Fast Algorithms for Indexing, Data-Mining and Visualization of Traditional and Multimedia Datasetsen_US
dc.typeTechnical Reporten_US

Files

Original bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
TR_94-80.pdf
Size:
1.24 MB
Format:
Adobe Portable Document Format