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040 _aDLC
_cUPMin
_dupmin
041 _aeng
090 0 _aLG993.5 2008
_bA64 M32
100 _aMabano, Armand Jay C.
_91987
245 _aEffectiveness of particles swarm optimization-tabu search (PSO-TS) to iris data set and wine data set /
_cArmand Jay C. Mabano.
260 _c2008
300 _a81 leaves.
502 _aThesis (BS Applied Mathematics) -- University of the Philippines Mindanao, 2008
520 3 _aData clustering is a process of grouping together similar objects in bins. This project aims to find alternative method of clustering continuous data set using a hybrid type of algorithm. The two algorithms that I tried to hybrid are Particle Swarm Optimization and Tabu Search. These two algorithms are used in many fields of clustering. There are a lot of literatures about these two algorithms embedded in other existing algorithms. The results show that the hybrid method is a good alternative for the pure PSO algorithms in finding an optimum solution for iris data set and wine data set. The graphs show the comparison between the hybrid algorithm and its pure counterpart. However, the parameter settings may not be the optimum settings and maybe improved. Another comparison was made between PSO-TS and PSO-SA (Particle Swarm Optimization ? Simulated Annealing). The result shows that the hybrid method were possible alternative for the pure one depends on the preferred criteria of the researcher. The criteria used for this study are optimal quantization error and solution time.
650 1 7 _aParticle swarm optimizations (PSO)
_91988
650 1 7 _aData clustering.
_91176
650 1 7 _aTabu search (TS)
_9943
650 1 7 _aQuantization errors.
_91989
650 1 7 _aPSO-TS (Particle Swarm Optimization)-(Tabu Search)
_91990
650 1 7 _aPSO-SA (particle Swarm Optimization)-(Simulation Annealing)
_91991
650 1 7 _aData sets.
_91992
650 1 7 _aData sets
_xIris.
_91993
650 1 7 _aData sets
_xWine.
_91994
650 1 7 _aAlgorithms.
_91336
658 _aUndergraduate Thesis
_cAMAT200
905 _aFi
905 _aUP
942 _2lcc
_cTHESIS
999 _c2239
_d2239