A new iterative fuzzy clustering algorithm for multiple imputation of missing data

Sanaz Nikfalazar, Chung Hsing Yeh, Susan Bedingfield, Hadi A. Khorshidi

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    13 Citations (Scopus)

    Abstract

    This paper proposes a new iterative fuzzy clustering (IFC) algorithm to impute missing values of datasets. The information provided by fuzzy clustering is used to update the imputed values through iterations. The performance of the IFC algorithm is examined by conducting experiments on three commonly used datasets and a case study on a city mobility database. Experimental results show that the IFC algorithm not only works well for datasets with a small number of missing values but also provides an effective imputation result for datasets where the proportion of missing data is high.

    Original languageEnglish
    Title of host publicationIEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2017
    EditorsHani Hagras , Francisco Herrera
    PublisherIEEE, Institute of Electrical and Electronics Engineers
    Pages1-6
    Number of pages6
    ISBN (Electronic)9781509060344
    DOIs
    Publication statusPublished - 23 Aug 2017
    EventIEEE International Conference on Fuzzy Systems 2017 - Naples, Italy
    Duration: 9 Jul 201712 Jul 2017
    Conference number: 26th
    https://ieeexplore.ieee.org/xpl/conhome/8011834/proceeding (Proceedings)

    Conference

    ConferenceIEEE International Conference on Fuzzy Systems 2017
    Abbreviated titleFUZZ-IEEE 2017
    CountryItaly
    CityNaples
    Period9/07/1712/07/17
    Internet address

    Keywords

    • Fuzzy clustering
    • Iterative algorithm
    • Missing values
    • Multiple imputation

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