Pengelompokan Data Menggunakan Pattern Reduction Enhanced Ant Colony Optimization dan Kernel Clustering

Dwi Taufik Hidayat, Chastine Fatichah, R.V. Hari Ginardi


One method of optimization that can be used for clustering is Ant Colony Optimization (ACO). This method is good in data clustering, but has disadvantage in terms of time and quality or solution convergence. In this study, ACO-based Pattern Reduction Enhanced Ant Colony Optimization (PREACO) method with a gaussian kernel function is proposed. First, it sets up initial solution. Second, the magnitude of pheromone is calculated to find the centroid randomly. With the initialized solution, the weight of the solution is calculated and the center of cluster is revised. The solution will be evaluated through a gaussian kernel functions. Function 'pattern enhanced reduction' is useful to ensure maximum value of pheromone update. Those steps will be conducted repeatedly until the best solution is chosen. Tests are performed on multiple datasets, with three test scenarios. The first test is carried out to get the right combination of parameters. Second, the error rate measurement and similarity data using Sum of Squared Errors is done. Third, level of accuracy of the methods ACO, ACO with the kernel, PREACO, and PREACO with the kernel is compared. The test results show that the proposed method has a higher accuracy rate of 99.8% for synthetic data, 93.8% for wine data than other methods. But it has a lower accuracy by 88.7% compared to the ACO.

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P.N. Tan, M. Steinbach, V. Kumar, Introduction to Data Mining. Pearson Education, Inc., Boston. (567-575), 2006.

P.B.S. Wiguna, B.S.Hartono, "Peningkatan Algoritme Porter Stemmer Bahasa Indonesia berdasarkan Metode Morfologi dengan Mengaplikasikan Tingkat Morfologi dan Aturan Kombinasi Awalan dan Akhiran," JNTETI, Vol. 2, No. 2, hal. 1-6 , Mei 2013.

N.F. Azzahra, H. Ginardi, A. Saikhu, “Praproses Data Alir ADS-B dari Multi-Receiver dengan Pengelompokan Agglomerasi Berbasis Konsistensi Jarak” JNTETI, Vol. 4, No. 1, hal. 39-44, Februari 2015.

I. Gath, A.B. Geva, “Unsupervised Optimal Fuzzy Clustering”, IEEE Trans. Pattern Anal, Machine Intell, No. 11, hal.773-780, 1989.

A. Lorette, X. Descombes, J. Zerubia, “Fully Unsupervised Fuzzy Clustering With Entropy Criterion”, 15Th International Conference on Pattern Recognition, IEEE, hal. 986-989, 2000.

D. Pelleg, A. Moore, “X-means: extending k-means with efficient estimation of the number of clusters”, Proceedings of the Seventeenth International Conference on Machine Learning, San Fransisco, hal. 727-734, 2000.

G. Hamerly, C. Elkan, “Learning the K in K-means, in:7th Annual Conference on Neural Information Processing Systems (NIPS)”, Vancouver and Whistler, British Columbia, Canada, hal. 281-288, 2003.

S. Das, A. Abraham, A. Konar, “Automatic kernel clustering with a multi-elitist particle swarm optimization algorithm”, Pattern Recogn. Lett. 29, hal. 688–699, 2008.

M. Tushir, S. Srivastava, “A New Kernelized Hybrid c-mean clustering model with optimized parameters”, Appl. Soft Comput, No. 10, hal. 381-389, 2010.

R.J. Kuo, Y.D. Huang,C.C. Lin, Y.H. Wu, F.E. Zulvia, 2014, “Automatic kernel clustering with bee colony optimization algorithm,” Information Sciences, 283, hal. 107–122, 2014.

Marco Dorigo and L.M. Gambardella, “Ant colony system: a cooperative learning approach to the tracelling salesman problem”, IEEE Transaction on Evolutionary Computation,1997(1), hal. 53-66.

C. Aditi, A. Darade, Swarm Intelligence Techniques: Comparative Study of ACO and BCO, Mumbay Unversity, 1998.

Chun-Wei Tsai, S.P. Tseng, C.S. Yang, M.C. Chiang, “PREACO: A fast ant colony optimization for codebook generation,” Applied Soft Computing 13, hal. 3008–3020, 2013.



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