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A hybrid data structure for searching in metric spaces
| Content Provider | Semantic Scholar |
|---|---|
| Author | Herrera, Norma Reyes, Nora Susana Chávez, Edgar |
| Copyright Year | 2004 |
| Abstract | The concept of “approximate” searching has applications in a vast number of fields. Some examples are non-traditional databases (e.g. storing images, fingerprints or audio clips, where the concept of exact search is of no use and we search instead for similar objects), text searching, information retrieval, machine learning and classification, image quantization and compression, computational biology, and function prediction. All those applications have some common characteristics. There is a universe of objects, and a nonnegative distance function defined among them. This distance satisfies the three axioms that make the set a metric space: strict positiveness ( ), symmetry ( ! " # $ % ! & ) and triangle inequality ( (') +*, .-/ ! (') ). The smaller the distance between two objects, the more “similar” they are. We have a finite database 0,1% , which is a subset of the universe of objects and can be preprocessed (to build an index, for example). Later, given a new object from the universe (a query 2 ), we must retrieve all similar elements found in the database. There are two typical queries of this kind: |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://sedici.unlp.edu.ar/bitstream/handle/10915/21227/Documento_completo.pdf?sequence=1 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
