00000ctm a22000003a 4500 UP-8027390931316196217 Buklod 20090825130705.0 a r |||| u| ta 090825s xx d r |||| u| (iLib)UPMIN-00003211653 DLC DLC upmin eng LG993.5 2008 A64 A66 Aporbo, Jenelyn Calderon. A K-nearest neighbor imputation method based on locally weighted scatterplot smoothing (LOESS) for ordinal data. 2008 2008 54 leaves. Thesis (BS Applied Mathematics) -- University of the Philippines Mindanao, 2008 This study introduced a modification of the classical k-nearest neighbor algorithm called Weighted KNN based on Locally Weighted Seatterplot Smoothing (LOESS). It uses weighting scheme to make sure that the degree of influence of each neighbor is accounted in estimating missing values. The newly developed imputation technique was evaluated and compared to the existing methods using Dermatology and Breast Cancer data sets. The incomplete data sets in the experiment were generated under MCAR condition only with 1%, 5%, 10% and 15% levels of missing values. K-means and k-modes clustering algorithms were used to determine the recovery of each technique. Results showed that Weighted KNN based on LOESS performed best when applied on both Dermatology and Breast Cancer data sets with K-means clustering algorithm. It ranked next to KNN when applied on Dermatology data set with k-modes clustering algorithm and other outperformed the rest of the techniques on Breast Cancer data set. In general, Weighted KNN based on LOESS showed promising results when tested on both data sets. Clustering. Imputation. K-means. Algorithms. K-nearest neighbor algorithm. LOESS (Locally Weighted Scatterplot Smoothing) KNN (K-nearest neighbor) Undergraduate Thesis AMAT200 BSAM. UP FI UPMIN UPMIN-MAIN LG993.5 2008 A64 A66 Thesis