000			02129nam a22002893a 4500
001			UPMIN-00000518117
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008			230209b |||||||| |||| 00| 0 eng d
040			_aDLC _cUPMin _dupmin
041			_aeng
090		0	_aLG993.5 2007 _bA64 S27
100			_aSarmiento, Jon Marx P. _9561
245			_a D-neighborhood imputation method for ordinal data sets with missing values / _cJon Marx P. Sarmiento
260			_c2007
300			_a111 leaves.
500			_aThesis (BS Applied Mathematics) -- University of the Philippines Mindanao, 2007
520	3		_aImputation 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.
650	1	7	_aClustering. _9366
650	1	7	_aK-means algorithm. _92351
650	1	7	_aImputation techniques. _92352
650	1	7	_aOrdinal data sets. _92353
658			_aUndergraduate Thesis _cAMAT200, _2BSAM
905			_aFi
905			_aUP
942			_2lcc _cTHESIS
999			_c699 _d699