Dealing with Missing Values in Data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21110%2F14%3A00222115" target="_blank" >RIV/68407700:21110/14:00222115 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.si-journal.org/index.php/JSI/article/view/178" target="_blank" >http://www.si-journal.org/index.php/JSI/article/view/178</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Dealing with Missing Values in Data

Popis výsledku v původním jazyce

Many existing industrial and research data sets contain missing values due to various reasons, such as manual data entry procedures, equipment errors and incorrect measurements. Problems associated with missing values are loss of efficiency, complications in handling and analyzing the data and bias resulting from differences between missing and complete data. The important factor for selection of approach to missing values is missing data mechanism. There are various strategies for dealing with missingvalues. Some analytical methods have their own approach to handle missing values. Data set reduction is another option. Finally missing values problem can be handled by missing values imputation. This paper presents simple methods for missing values imputation like using most common value, mean or median, closest fit approach and methods based on data mining algorithms like k-nearest neighbor, neural networks and association rules, discusses their usability and presents issues with their

Název v anglickém jazyce

Dealing with Missing Values in Data

Popis výsledku anglicky

Many existing industrial and research data sets contain missing values due to various reasons, such as manual data entry procedures, equipment errors and incorrect measurements. Problems associated with missing values are loss of efficiency, complications in handling and analyzing the data and bias resulting from differences between missing and complete data. The important factor for selection of approach to missing values is missing data mechanism. There are various strategies for dealing with missingvalues. Some analytical methods have their own approach to handle missing values. Data set reduction is another option. Finally missing values problem can be handled by missing values imputation. This paper presents simple methods for missing values imputation like using most common value, mean or median, closest fit approach and methods based on data mining algorithms like k-nearest neighbor, neural networks and association rules, discusses their usability and presents issues with their

Druh

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

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

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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 periodika

Journal of Systems Integration

ISSN

1804-2724

e-ISSN

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Svazek periodika

5

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

10

Strana od-do

42-51

Kód UT WoS článku

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EID výsledku v databázi Scopus

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