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  1. 1

    Modified principal feature analysis (MPFA) as a feature selection algorithm for clustering large data sets within missing values. 由 Aguelo, Renz Marion Y.

    出版 2010
    Thesis

    Modified principal feature analysis (MPFA) as a feature selection algorithm for clustering large data sets within missing values.
  2. 2

    Modified K-mean clustering algorithm for fixed numeric and categorical data sets with missing values 由 Madarang, Jennelle Rizza M.

    出版 2010
    Thesis

    Modified K-mean clustering algorithm for fixed numeric and categorical data sets with missing values
  3. 3

    Exploratory analysis on DPCoA on small data sets with missing values using imputation methods 由 Mahinay, Kristine Karen C.

    出版 2010
    Thesis

    Exploratory analysis on DPCoA on small data sets with missing values using imputation methods
  4. 4

    Data Clustering system on categorical data using modified K-modes clustering algorithms 由 Parreño, Cherry Lyn N.

    出版 2009
    Thesis

    Data Clustering system on categorical data using modified K-modes clustering algorithms
  5. 5

    Fuzzy Jaccard similarity approach in handling missing values for randomly amplified polymorphic DNA (RAPD) analysis 由 Ubas, Apple Grace Otero

    出版 2008
    Thesis

    Fuzzy Jaccard similarity approach in handling missing values for randomly amplified polymorphic DNA (RAPD) analysis
  6. 6

    Clustering of datasets with missing values using principal feature analysis as a feature selection tool 由 Pelpinosas, Frank B.

    出版 2008
    Thesis

    Clustering of datasets with missing values using principal feature analysis as a feature selection tool
  7. 7

    A modified K-modes algorithm for clustering categorical data sets with missing values using bhattacharyya distance function 由 Gabiana, Marie Lou Manalili

    出版 2006
    Thesis

    A modified K-modes algorithm for clustering categorical data sets with missing values using bhattacharyya distance function

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