Data Cleansing for Data Quality Improvement in Data Mining

    Research output: ThesisDoctoral Thesis

    62 Downloads (Pure)

    Abstract

    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
    Supervisors/Advisors
    • Islam, Zahid, Principal Supervisor
    • Bossomaier, Terry, Co-Supervisor
    Award date20 Jun 2015
    Place of PublicationAustralia
    Publisher
    Publication statusPublished - 2015

    Fingerprint Dive into the research topics of 'Data Cleansing for Data Quality Improvement in Data Mining'. Together they form a unique fingerprint.

  • Cite this