000			02214nam a2200241 4500
001			UPMIN-00000010897
003			UPMIN
005			20230201165228.0
008			230201b |||||||| |||| 00| 0 eng d
040			_aDLC _cUPMin _dupmin
041			_aeng
090			_aLG993.5 2000 _bA64 M34
100	1		_aMacabenta, Mel Zha Leah M. _92020
245	0	0	_aClustering of data sets with missing values using statistical imputation methods / _c Mel Zha Leah M.Macabenta.
260			_c2000
300			_a65 leaves.
502			_aThesis (BS Applied Mathematics) -- University of the Philippines Mindanao, 2000
520	3		_aK-means clustering algorithm is the most widely used clustering algorithm in the field of data analysis. One major drawback of this algorithm is that it can never accommodate data set with missing values. However, in reality, occurrence of missing values can not be avoided. Imputation methods are more extensively used in treating missing values compared to deletion. Several imputation methods are suggested but each has advantages and disadvantages over the others, so proper choice of imputation methods is very necessary. Two of the statistical imputation methods namely, hot deck imputation and imputation using a prediction model were used in treating the incomplete data sets. The incomplete data sets after treatment were then clustered using the K-means clustering algorithm. To have a clear comparison, five data sets were used with two kinds of missingness, missing completely at random (MCAR) and missing at random (MAR) at five different levels of degradation ranging from 1% missing values. The evaluation of the resulting clusters was done using the adjusted Rand index. The two methods were compared to the modified K-means algorithm, particularly the modified Euclidean distance. Results showed that the hot deck imputation, regression method and modified K-means clustering algorithm attained a high recovery of clusters especially with big data sets until 30% levels of missing values. In small data sets, good recovery is attained until 10% level of missing values only.
658			_aUndergraduate Thesis _cAMAT200
905			_aFi
905			_aUP
942			_2lcc _cTHESIS
999			_c532 _d532