000			02105nam a2200277 4500
001			UPMIN-00000014631
003			UPMIN
005			20230209114954.0
008			230209b |||||||| |||| 00| 0 eng d
040			_aDLC _cUPMin _dupmin
041			_aeng
090			_aLG993.5 2006 _bA64 R47
100	1		_aResponso, Crisemhar Robledo. _92257
245	0	0	_aA particle swarm optimization-simulated annealing (PSO-SA) hybrid for data clustering / _cCrisemhar Robledo Responso.
260			_c2006
300			_a84 leaves
502			_aThesis (BS Applied Mathematics) -- University of the Philippines Mindanao, 2006
520	3		_aData clustering is a problem that deals with classification of objects within the data set into clusters such that items in the same cluster have a high degree of similarity. Known heuristic algorithms are applied to solve the problem. In this study, Particle Swarm Optimization (PSO) hybrid with Simulated Annealing (SA) was used to cluster data on Iris data set. PSO is relatively new family of algorithm, which is a population ?based stochastic optimization technique while SA is an algorithm, which is a population-based stochastic optimization technique while SA is an algorithm that concerns with finding global extremum of the function and works on a single solution. Different sets of parameter values were tested on the algorithm to determine which setting best suits the data. Results showed that smaller parameter values for SA and PSO parameters except of inertia weight performed significantly faster while larger parameter values of all parameter except inertia gave better solution quality. The result also showed that number of hits or assignment of data to a cluster is somewhat bad. However, PSO-SA algorithm is still a promising alternative to cluster data on Iris data set if further improvements can be done.
650			_aData clustering. _91176
650			_aParticle Swarm Optimization(PSO). _92258
650			_aSimulated annealing. _91370
658			_aUndergraduate Thesis _cAMAT200, _2BSAM
905			_aFi
905			_aUP
942			_2lcc _cTHESIS
999			_c664 _d664