00000ctm a22000003a 4500 UP-8027390931316196214 Buklod 20090825110100.0 a r |||| u| ta 090825s xx d r |||| u| (iLib)UPMIN-00003211650 DLC LC upmin eng LG993.5 2008 A64 P44 Pelpinosas, Frank B. Clustering of datasets with missing values using principal feature analysis as a feature selection tool Frank B. Pelpinosas. 2008 51 leaves. Thesis (BS Applied Mathematics) -- University of the Philippines Mindanao, 2008 One of the most prevalent problems One of the most prevalent problems in clustering is the presence of redundant and irrelevant features, which could damage and misguide the clustering results of the data. Principal Feature Analysis is used as a filter feature selection tool to reduce highly dimensional datasets into smaller dimensions yet preserving the original structure of the data. The problem is worsened with the presence of missing values in the data. The study provides a comparison of the clustering results of the complete (base) datasets and imputed datasets using K-NN and mean imputation across three levels of degradation. The features retained by PFA were used to cluster the samples and were assessed using the Adjusted Rand Index. Results showed that PFA indeed had reduced the dimensions of the data. Principal Feature Analysis also can hardly drop some feature seven when charges in the levels of degradation appear. Both feature retention and cluster recovery were negatively affected by the number of missing values in the data in all the comparison Clustering. Feature selections. Missing values. PFA Principal feature analysis. Datasets. Adjusted Rand Index. MCAR (Missing completely at random) Undergraduate Thesis AMAT200 BSAM. FI UP UPMIN UPMIN-MAIN LG993.5 2008 A64 P44 Thesis