Data mining a kvalita dat

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F05%3A00001714" target="_blank" >RIV/62690094:18450/05:00001714 - 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

Data mining and data cleaning

Popis výsledku v původním jazyce

This paper deals with the importance of data quality in the data mining process. Organizations rely upon their data from which information and knowledge are produced for business decision-making. The quality of the results achieved through analysis is highly correlated with the quality of the inputs - data. The efficient implementation of the data mining algorithms and the interpretation of the results are of little value unless any techniques to detect and remove bad data are applied. There are many different ways how the data can become bad. It can be inaccurate, inconsistent, incomplete and out of date. Data can be also changed both deliberately or accidentally. Bad data results in wrong decision-making, which can damage the business, therefore datacleaning should be an integral part of analysis. The organizations with cleaner data and the means to use that data will gain a competitive advantage. The techniques to help ensure data quality by data cleaning and applying data quality

Název v anglickém jazyce

Data mining and data cleaning

Popis výsledku anglicky

This paper deals with the importance of data quality in the data mining process. Organizations rely upon their data from which information and knowledge are produced for business decision-making. The quality of the results achieved through analysis is highly correlated with the quality of the inputs - data. The efficient implementation of the data mining algorithms and the interpretation of the results are of little value unless any techniques to detect and remove bad data are applied. There are many different ways how the data can become bad. It can be inaccurate, inconsistent, incomplete and out of date. Data can be also changed both deliberately or accidentally. Bad data results in wrong decision-making, which can damage the business, therefore datacleaning should be an integral part of analysis. The organizations with cleaner data and the means to use that data will gain a competitive advantage. The techniques to help ensure data quality by data cleaning and applying data quality

Druh

D - Stať ve sborníku

CEP obor

BB - Aplikovaná statistika, operační výzkum

OECD FORD obor

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Projekt

<a href="/cs/project/GA402%2F04%2F1308" target="_blank" >GA402/04/1308: Klasifikační modely a porovnání jejich prediktivních vlastností</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2005

Kód důvěrnosti údajů

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

Název statě ve sborníku

Imea

ISBN

80-7083-929-5

ISSN

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e-ISSN

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Počet stran výsledku

7

Strana od-do

1-7

Název nakladatele

Technická univerzita v Liberci

Místo vydání

Liberec

Místo konání akce

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Datum konání akce

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Typ akce podle státní příslušnosti

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Kód UT WoS článku

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