Local representativeness in vector data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F14%3A86099406" target="_blank" >RIV/61989100:27240/14:86099406 - isvavai.cz</a>

Výsledek na webu

<a href="http://ieeexplore.ieee.org/document/6974025/?arnumber=6974025" target="_blank" >http://ieeexplore.ieee.org/document/6974025/?arnumber=6974025</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/SMC.2014.6974025" target="_blank" >10.1109/SMC.2014.6974025</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Local representativeness in vector data

Popis výsledku v původním jazyce

The amount of large-scale real data around us is increasing in size very quickly, as is the necessity to reduce its size by obtaining a representative sample. Such sample allows us to use a great variety of analytical methods, the direct application of which on original data would be unfeasible. Conventional sampling methods provide non-deterministic results trying to preserve selected characteristics of the input dataset. We present a novel, simple, straightforward and deterministic approach with the same goal. It is not sampling in the true sense but a reduction of vector data, which maintains very well internal data structures (clusters and density). The approach is based on analyzing the nearest neighbors. Our suggested x-representativeness then takes into account the local density of the data and nearest neighbors of individual data objects. Following that, we also present experiments with two different datasets. The aim of these experiments is to show that the x-representativeness can be used to deterministically reduce the datasets to differently sized samples of representatives, while maintaining properties of the original datasets. (C) 2014 IEEE.

Název v anglickém jazyce

Local representativeness in vector data

Popis výsledku anglicky

The amount of large-scale real data around us is increasing in size very quickly, as is the necessity to reduce its size by obtaining a representative sample. Such sample allows us to use a great variety of analytical methods, the direct application of which on original data would be unfeasible. Conventional sampling methods provide non-deterministic results trying to preserve selected characteristics of the input dataset. We present a novel, simple, straightforward and deterministic approach with the same goal. It is not sampling in the true sense but a reduction of vector data, which maintains very well internal data structures (clusters and density). The approach is based on analyzing the nearest neighbors. Our suggested x-representativeness then takes into account the local density of the data and nearest neighbors of individual data objects. Following that, we also present experiments with two different datasets. The aim of these experiments is to show that the x-representativeness can be used to deterministically reduce the datasets to differently sized samples of representatives, while maintaining properties of the original datasets. (C) 2014 IEEE.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

—

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2014

Kód důvěrnosti údajů

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

Název statě ve sborníku

Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics 2014

ISBN

978-1-4799-3840-7

ISSN

1062-922X

e-ISSN

—

Počet stran výsledku

6

Strana od-do

894-899

Název nakladatele

IEEE

Místo vydání

New York

Místo konání akce

San Diego

Datum konání akce

5. 10. 2014

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000370963701002