000			01789nam a22003133a 4500
001			UPMIN-00003211628
003			UPMIN
005			20230202144919.0
008			230202b |||||||| |||| 00| 0 eng d
040			_aDLC _cUPMin _dupmin
041			_aeng
090		0	_aLG993.5 2008 _bA64 M37
100			_aMarbas, Ivan Art F. _92070
245			_aClustering datasets with missing values using modified K-medoids algorithm / _cIvan Art F. Marbas.
260			_c2008
300			_a61 leaves.
502			_aThesis (BS Applied Mathematics) -- University of the Philippines Mindanao, 2008
520	3		_aA modification was done to the Euclidean distance to compute distance for incomplete data points, at the same time flagging them so that the algorithm will avoid choosing them as cluster medoids. This resulted to the Modified K-medoids clustering algorithm applied with pre-processing methods, namely, Case Deletion, Mean Imputation and K-nearest Neighbor Imputation, in clustering incomplete datasets, it showed that the proposed algorithm performs only second best to K-nearest Neighbor Imputation. The comparison was made using incomplete datasets generated from the Iris and Bupa dataset with different missing value occurrences and degradation levels. Though only second best, the production of cluster medoids with no missing values is unique to the modification. Thus, the Modified K-medoids clustering algorithm is more advantageous.
650	1	7	_aClustering. _9366
650	1	7	_aIncomplete datasets. _92071
650	1	7	_aK-medoids. _92072
650	1	7	_aModified Euclidiean distance. _92073
650	1	7	_aDatasets. _91958
650	1	7	_aEuclidean distance. _92074
658			_aUndergraduate Thesis _cAMAT200, _2BSAM
905			_aFi
905			_aUP
942			_2lcc _cTHESIS
999			_c2225 _d2225