Data Cleaning in Knowledge Discovery Database-Data Mining (KDD-DM)
Fauziah Abdul Rahman1, Rahimah Kassim2, Zirawani Baharum3, Helmi Adly Mohd Noor4, Norhaidah Abu Haris5
1Fauziah Abdul Rahman, Universiti Kuala Lumpur-Malaysian Institute of Information Technology, Kuala Lumpur, Malaysia.
2Rahimah Kassim, Universiti Kuala Lumpur-Malaysian Institute of Information Technology, Kuala Lumpur, Malaysia.
3Zirawani Baharum, Universiti Kuala Lumpur-Malaysian Institute of Information Technology, Kuala Lumpur, Malaysia.
4Helmi Adly Mohd Noor, Universiti Kuala Lumpur-Malaysian Institute of Information Technology, Kuala Lumpur, Malaysia.
5Norhaidah Abu Haris, Universiti Kuala Lumpur-Malaysian Institute of Information Technology, Kuala Lumpur, Malaysia.
Manuscript received on 01 November 2019 | Revised Manuscript received on 13 November 2019 | Manuscript Published on 22 November 2019 | PP: 2196-2199 | Volume-8 Issue-6S3 September 2019 | Retrieval Number: F11000986S319/19©BEIESP | DOI: 10.35940/ijeat.F1100.0986S319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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 (DC). DC is a process used to determine inaccurate, incomplete or unreasonable data and then improve the quality through correcting of detected errors and omissions. Various process of DC have been discussed in the previous studies, but there is no standard or formalized the DC process. The Domain Driven Data Mining (DDDM) is one of the KDD methodology often used for this purpose. This paper review and emphasize the important of DC in data preparation. The future works was also being highlight.
Keywords: Data Cleaning, Data Mining, Missing Value.
Scope of the Article: Data Mining