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  1. 1

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

    Izdano 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 od Madarang, Jennelle Rizza M.

    Izdano 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 od Mahinay, Kristine Karen C.

    Izdano 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 od Parreño, Cherry Lyn N.

    Izdano 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 od Ubas, Apple Grace Otero

    Izdano 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 od Pelpinosas, Frank B.

    Izdano 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 od Gabiana, Marie Lou Manalili

    Izdano 2006
    Thesis

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

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