TY  - BOOK
AU  - Sarmiento, Jon Marx P.
TI  -  D-neighborhood imputation method  for ordinal data sets with missing values
PY  - 2007///
KW  - Clustering
KW  - K-means algorithm
KW  - Imputation techniques
KW  - Ordinal data sets
KW  - Undergraduate Thesis
KW  - AMAT200,
KW  - BSAM
N1  - Thesis (BS Applied Mathematics) -- University of the Philippines Mindanao, 2007
N2  - Imputation is applied in filling up missing values in surveys which are ordinal in form. Among the imputation techniques are Mean, Mode, Hot-deck and KNN imputations which have their own drawbacks. To address this issue, the proponent introduced a new imputation method called D-neighborhood imputation. It uses the concept of neighborhood and cut off value to ensure high similarity with the reference and the maximum penalty rule in solving for the distance of unknown values. D-neighborhood was evaluated and compared with the existing techniques. The experiment was done using the Dermatology and Breast Cancer data sets. Incomplete data sets were generated under MCAR with 1%, 5%, 10%, 20%, and 30% level of missing values and conditioned MCAR with 0.25, 0.5, 0.75 and 1 probability in no, 2, and 3 combinations. According to the results, it performed best under MCAR condition in both data sets and resulted the best clustering quality when applied to Breast Cancer data set under MAR condition. Using Dermatology data set, D-neighborhood and KNN have competing results while using Breast Cancer data set, D-neighborhood performed best. In general, D-neighborhood imputation outperformed the rest of the algorithms when tested in both data sets
ER  -