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040 _aDLC
_cUPMin
_dupmin
041 _aeng
090 0 _aLG 993.5 2011
_bC6 M38
100 _aMatunog, Mikael Nazal.
245 _aContinuous tabu-firefly algorithm applied to the K-means clustering problem /
_cMikael Nazal Matunog.
260 _c2011
300 _a133 leaves.
502 _aThesis (BS Computer Science) -- University of the Philippines Mindanao, 2011
520 3 _aData clustering is the unsupervised classification of unlabeled data objects into groups called clusters. It is one of the most primitive activities of human beings, and has been extensively used for understanding and utility. One type of clustering is K-means clustering, where data objects are partitioned int multiple clusters. This paper proposed a new approach in solving the K-means clustering problem using a novel hybrid of Continuous Tabu Search (CTS) and a modified Firefly Algorithm (FA). The new algorithm, called Continuous Tabu-Firefly Algortihm (CTFA), used the CTS as a local search method embedded in the move operator of the modified FA. CTFA was tested against the pure Firefly Algorithm and the Hybrid K-means and Particle Swarm Optimization. The performance of each algorithm was benchmarked using the Iris and Wine data sets. The results of the study show that CTFA was able to surpass the clustering efficiency of both algorithms in terms of solution quality. With regards to solution time, CTFA took longer to generate the solution. However, CTFA still has shorter solution time compared to other brute force methods.
650 1 7 _aAlgorithms.
650 1 7 _aFirefly Algorithm (FA)
650 1 7 _aContinuous Tabu Searchn (CTS)
650 1 7 _aK-means.
650 1 7 _aClustering.
650 1 7 _aHybrid metaheuristics.
650 1 7 _aParticle swarm optimization.
650 1 7 _aK-means algorithm.
650 1 7 _aClustering problem.
650 1 7 _aContinuous Tab-Firefly Algorithm (CTFA)
650 1 7 _aBrute force methods.
658 _aUndergraduate Thesis
_cCMSC200,
_2BSCS
905 _aFi
905 _aUP
942 _2lcc
_cTHESIS
999 _c2680
_d2680