An Improved Technique for the Removal and Replacement of the Inconsistencies in Numeric Dataset

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dc.contributor.authorAbdul-Hadi, J.
dc.contributor.authorAjiboye, A.R.
dc.contributor.authorAbba, A.
dc.date.accessioned2018-12-03T13:23:56Z
dc.date.available2018-12-03T13:23:56Z
dc.date.issued2015-05
dc.descriptionArticleen_US
dc.description.abstractThe task of ensuring the removal of anomalies in an unclean numeric dataset, with a view to putting the data in a suitable format for exploration purposes is a major phase in the data mining process. In the process of exploring an unclean numeric dataset to unveil their useful patterns or structure, a thorough pre-processing task is inevitable in order to achieve a noise-free dataset. Poor quality data can be misleading if analysed or used to build models, hence, there is need to remove discrepancies that may be present in the data prior to exploring them. In this paper, a cleaning algorithm is proposed and implemented in order to remove the inconsistencies in a numeric dataset. The implementation of the proposed algorithm uses the Java language and the resulting outputs reveal the efficiency of the proposed approach. In order to evaluate the effectiveness of the proposed algorithm, it is compared to one of the existing methods based on some metrics. The comparisons show that, the proposed technique is efficient and can be used as an alternative technique for the removal of outliers in numeric data. This approach is also found to be reliable as it consistently gives an accurate output that is free of outliers.en_US
dc.identifier.citationAfrican Journal of Computing & ICTen_US
dc.identifier.issn2006-1781
dc.identifier.urihttp://hdl.handle.net/123456789/1333
dc.language.isoenen_US
dc.publisherIEEE Nigeria Chapter.en_US
dc.relation.ispartofseriesIssue 2;Vol 8. No. 1
dc.subjectData cleansingen_US
dc.subjectData miningen_US
dc.subjectOutlier detectionen_US
dc.subjectClusteringen_US
dc.titleAn Improved Technique for the Removal and Replacement of the Inconsistencies in Numeric Dataseten_US
dc.typeArticleen_US

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