Data Cleansing for Data Quality Improvement in Data Mining

    Research output: ThesisDoctoral Thesis

    79 Downloads (Pure)


    Good quality data are essential in data mining. Main tasks of data cleansing include missing value imputation and noisy value detection. We propose missing value imputation techniques that can automatically and accurately guesstimate missing values in data. We also propose a noise detection technique that automatically identifies data that are noisy or incorrect. If we clean up a dataset by our techniques then conventional data mining techniques such as a decision tree classifier are more likely to build better models.
    Original languageEnglish
    QualificationDoctor of Philosophy
    Awarding Institution
    • Charles Sturt University
    • Islam, Zahid, Principal Supervisor
    • Bossomaier, Terry, Co-Supervisor
    Award date20 Jun 2015
    Place of PublicationAustralia
    Publication statusPublished - 2015

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