000			02962nam a22002893a 4500
001			UPMIN-00005135667
003			UPMIN
005			20221205145426.0
008			221205b |||||||| |||| 00| 0 eng d
040			_aDLC _cUPMin _dupmin
041			_aeng
090		0	_aLG993.5 2010 A64 _bC66
100			_aComendador, Christine Y. _9363
245			_aMonothetic clustering algorithms based on measures of association / _cChristine Y. Comendador
260			_c2010
300			_a111 leaves
500			_aThesis, Undergraduate (BS in Applied Mathematics - Operations Research) -- U.P. Mindanao
520	3		_aCluster analysis is one common technique that is used in multivariate analysis. Its objective is to maximize the dissimilarity of objects within the sane cluster and maximize the dissimilarity of object among different clusters. Monothetic analysis is a class of methods used for clustering binary data. Monothetic analysis algorithms produce a hierarchy of clusters in which each step a group is split into two clusters based on the values of one of the binary variables. However, existing monothetic algorithms use the simplest association measure. This study was done to test whether Yule?s q, Yule's y, Phi coefficient and percent difference could be used as a splitting criterion. The algorithm was modified in such a way that variables having identical values for all observations were ignored to avoid undefined values for the association between variables and by replacing the standard criterion with the selected association measures. The modified algorithms were tested with twenty simulated binary data resulted in identifying the same number of cluster. Nevertheless, the algorithms varied in the selection of splitting variable and in effect, results to different number of iterations performed. Yule?s y ? based MONA performs the fastest algorithm among the modified algorithms and Phi coefficient ? based MONA performs the slowest algorithm in terms of the number of iterations. However, percentage difference ?based MONA yields the fastest algorithm compared to all other algorithms in terms of processing time. Aside from the simulated data sets, an application to the habitat of 16 species to 129 communities was also used in this study. As a result, it confirms the fact that Percent difference ?based MONA performs the fastest algorithm compared to all algorithms in terms of processing time. Also, it confirms the fact that Phi coefficient ?based MONA performs slowest algorithm. Despite the fact that Percent difference ?based MONA is the fastest algorithm on both application to data sets, further study are still needed to verify the performance of these modified algorithms
650	1	7	_aAssociation measures _9364
650	1	7	_aBinary data _9365
650	1	7	_aClustering _9366
650	1	7	_aMonothetic analysis _9367
658			_aUndergraduate Thesis _cAMAT200
905			_aFi
905			_aUP
942			_2lcc _cTHESIS
999			_c2514 _d2514