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Data Cleaning in Knowledge Discovery Database (KDD)-Data Mining

Conference venue: Kuala Lumpur

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Bibliographic Details
Main Authors: Fauziah Abdul Rahman, Mohammad Ishak Desa, Antoni Wibowo, Norhaidah Abu Haris
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Published: 2014
Subjects:
Data Cleaning
Data Mining
Missing Value
DC Process
Online Access:http://localhost/xmlui/handle/123456789/5879
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id my.unikl.ir-5879
record_format eprints
spelling my.unikl.ir-58792014-04-10T01:37:00Z Data Cleaning in Knowledge Discovery Database (KDD)-Data Mining Fauziah Abdul Rahman Mohammad Ishak Desa Antoni Wibowo Norhaidah Abu Haris Data Cleaning Data Mining Missing Value DC Process Conference venue: Kuala Lumpur Data quality is a main issue in quality information management. Data quality problems occur anywhere in information systems. These problems are solved by data cleaning. Data cleaning (DC)is a process used to determine inaccurate, incomplete or unreasonable data and then improve the quality through correcting of detected errors and omissions. Generally data cleaning reduces errors and improves the data quality. It is well known that the process of correcting errors in data and eliminating bad records are time consuming and involve a tedious process but it cannot be ignored. Various process of DC have been discussed in the previous studies, but there’s no standard or formalized the DC process. Knowledge Discovery Database (KDD) is a tool that enables one to intelligently analyze and explore extensive data for effective decision making. The Cross-Industry Standard Process for Data Mining (CRISP-DM) is one of the KDD methodology often used for this purpose. This paper review and emphasize the important of DC in data preparation. The wrong analysis will probably turn out to be expensive failures. The future works was also being highlighted. 2014-04-10T01:37:00Z 2014-04-10T01:37:00Z 2014-04-10 http://localhost/xmlui/handle/123456789/5879
institution Universiti Kuala Lumpur
building UniKL Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Kuala Lumpur
content_source UniKL Institutional Repository
url_provider http://ir.unikl.edu.my/
topic Data Cleaning
Data Mining
Missing Value
DC Process
spellingShingle Data Cleaning
Data Mining
Missing Value
DC Process
Fauziah Abdul Rahman
Mohammad Ishak Desa
Antoni Wibowo
Norhaidah Abu Haris
Data Cleaning in Knowledge Discovery Database (KDD)-Data Mining
description Conference venue: Kuala Lumpur
format
author Fauziah Abdul Rahman
Mohammad Ishak Desa
Antoni Wibowo
Norhaidah Abu Haris
author_facet Fauziah Abdul Rahman
Mohammad Ishak Desa
Antoni Wibowo
Norhaidah Abu Haris
author_sort Fauziah Abdul Rahman
title Data Cleaning in Knowledge Discovery Database (KDD)-Data Mining
title_short Data Cleaning in Knowledge Discovery Database (KDD)-Data Mining
title_full Data Cleaning in Knowledge Discovery Database (KDD)-Data Mining
title_fullStr Data Cleaning in Knowledge Discovery Database (KDD)-Data Mining
title_full_unstemmed Data Cleaning in Knowledge Discovery Database (KDD)-Data Mining
title_sort data cleaning in knowledge discovery database (kdd)-data mining
publishDate 2014
url http://localhost/xmlui/handle/123456789/5879
_version_ 1644484795134115840
score 13.222552

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