On Fuzzy vs. metric similarity search in complex databases

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F09%3A10109181" target="_blank" >RIV/00216208:11320/09:10109181 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

On Fuzzy vs. metric similarity search in complex databases

Popis výsledku v původním jazyce

The task of similarity search is widely used in various areas of computing, including multimedia databases, data mining, bioinformatics, social networks, etc. For a long time, the database-oriented applications of similarity search employed the definition of similarity restricted to metric distances. Due to the metric postulates (reflexivity, non-negativity, symmetry and triangle inequality), a metric similarity allows to build a metric index above the database which can be subsequently used for efficient (fast) similarity search. On the other hand, the metric postulates limit the domain experts (providers of the similarity measure) in similarity modeling. In this paper we propose an alternative non-metric method of indexing for efficient similarity search. The requirement on metric is replaced by the requirement on fuzzy similarity satisfying the transitivity property with a tuneable fuzzy conjunctor. We also show a duality between the fuzzy approach and the metric one.

Název v anglickém jazyce

On Fuzzy vs. metric similarity search in complex databases

Popis výsledku anglicky

The task of similarity search is widely used in various areas of computing, including multimedia databases, data mining, bioinformatics, social networks, etc. For a long time, the database-oriented applications of similarity search employed the definition of similarity restricted to metric distances. Due to the metric postulates (reflexivity, non-negativity, symmetry and triangle inequality), a metric similarity allows to build a metric index above the database which can be subsequently used for efficient (fast) similarity search. On the other hand, the metric postulates limit the domain experts (providers of the similarity measure) in similarity modeling. In this paper we propose an alternative non-metric method of indexing for efficient similarity search. The requirement on metric is replaced by the requirement on fuzzy similarity satisfying the transitivity property with a tuneable fuzzy conjunctor. We also show a duality between the fuzzy approach and the metric one.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2009

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ISSN

0302-9743

e-ISSN

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Svazek periodika

2009

Číslo periodika v rámci svazku

5822

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

12

Strana od-do

64-75

Kód UT WoS článku

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EID výsledku v databázi Scopus

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