클러스터 수가 주어지지 않는 클러스터링 문제를 위한 공생 진화알고리즘
A symbiotic evolutionary algorithm for the clustering problems with an unknown number of clusters
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Clustering is an useful method to classify objects into subsets that have some meaning in the context of a particular problem and has been applied in variety of fields, customer relationship management, data mining,pattern recognition, and biotechnology etc. This paper addresses the unknown K clustering problems and presents a new approach based on a coevolutionary algorithm to solve it. Coevolutionary algorithms are known as very efficient tools to solve the integrated optimization problems with high degree of complexity compared to classical ones. The problem considered in this paper can be divided into two sub-problems; finding the number of clusters and classifying the data into these clusters. To apply to coevolutionary algorithm, the framework of algorithm and genetic elements suitable for the sub-problems are proposed. Also, a neighborhood-based evolutionary strategy is employed to maintain the population diversity. To analyze the proposed algorithm, the experiments are performed with various test-bed problems which are grouped into several classes. The experimental results confirm the effectiveness of the proposed algorithm.
Abstract
Clustering is an useful method to classify objects into subsets that have some meaning in the context of a particular problem and has been applied in variety of fields, customer relationship management, data mining,pattern recognition, and biotechnology etc. This paper addresses the unknown K clustering problems and presents a new approach based on a coevolutionary algorithm to solve it. Coevolutionary algorithms are known as very efficient tools to solve the integrated optimization problems with high degree of complexity compared to classical ones. The problem considered in this paper can be divided into two sub-problems; finding the number of clusters and classifying the data into these clusters. To apply to coevolutionary algorithm, the framework of algorithm and genetic elements suitable for the sub-problems are proposed. Also, a neighborhood-based evolutionary strategy is employed to maintain the population diversity. To analyze the proposed algorithm, the experiments are performed with various test-bed problems which are grouped into several classes. The experimental results confirm the effectiveness of the proposed algorithm.
참고문헌 (26)
- 김성호, "인위적 데이터를 이용한 군집 분석 프로그램간의 비교에 대한 연구", 지능정보연구, 7(2), pp.35~49 (2001)
- 문숙경, "마케팅자료에서 특성점들을 이용한 군집방법", 품질경영학회지, 32(4), pp.265~273 (2004), 한국품질경영학회
- 오은녕, "클러스터링 기법을 이용한 이동통신의 고객 세분화 연구", 한국경영과학회 추계논문집, pp.421~424 (2002)
- 황인수, "데이터 마이닝에서 그룹 세분화를 위한 2단계 계층적 클러스터링 알고리듬", 경영과학, 19(1), pp.189~196 (2002), 한국경영과학회
- Al-Sultan, K., "A Tabu search approach to the clustering problem", Pattern Recognition, 28(9), pp.1443~1451 (1995)
- Bandyopadhyay, S., "An evolutionary technique based on K-Means algorithm for optimal clustering in RN", Information Sciences, 146, pp.221~237 (2002)
- Bandyopadhyay, S., "Genetic clustering for automatic evolution of clusters and application to image classification", Pattern Recognition, 35, pp.1197~1208 (2002)
- Brown, D., "A practical application of simulated annealing to clustering", Pattern Recognition, 25(4), pp.401~412 (1992)
- Cooley, R., "Web Mining: Information Pattern Discovery on the World Wide Web", Proc. of the 9th IEEE International Conference, pp.558~567 (1997)
- Davies, D.L., "A cluster separation measure", IEEE Transactions on Pattern Recognition and Machine Intelligence, 1(2), pp.224~227 (1979)
- Garai, G., "A novel genetic algorithm for automatic clustering", Pattern Recognition Letters, 25, pp.173~187 (2004)
- Halkidi, M., "On clustering validation techniques", Journal of Intelligent Information Systems, 17, pp.107~145 (2001)
- Hruschka E.R., "A survey of evolutionary algorithms for clustering", IEEE Transactions on Systems, Man, and Cybernetics-Part C: Applications and Reviews, 39(2), pp.133~155 (2009)
- Kim, Y.K., "A coevolutionary algorithm for balancing and sequencing in mixed model assembly lines", Applied Intelligence, 13, pp.247~258 (2000)
- Kim, Y.K., "A symbiotic evolutionary algorithm for the integration of process planning and job shop scheduling", Computers & Operations Research, 30, pp.1151~1171 (2003)
- Koontz, W.L.G., "A branch and bound clustering algorithm", IEEE Transactions on Computers, 24(9), pp.908~915 (1975)
- Liu, G., "Intoduction to combinatorial mathematics" (1968), McGraw-Hill
- Margulis, L., "Symbiosis in cell evolution" (1981), W.H.Freeman
- Maulik, U., "Genetic algorithm-based clustering technique", Pattern Recognition, 33, pp.1455~1465 (2000)
- Moriarty, D.E., "Forming neural networks through efficient and adaptive coevolution", Evolutionary Computation, 5, pp.373~399 (1997)
- Selim, S., "A simulated annealing algorithm for the clustering problems", Pattern Recognition, 24(10), pp.1003~1008 (1991)
- Sung, C., "A Tabu-search-based heuristic for clustering", Pattern Recognition, 33, pp.849~858 (2000)
- Tou, J.T., "Pattern Recognition Principles" (1974), Addison-Wesley
- Theodoridis, S., "Pattern Recognition, 3rd Edition" (2006), Academic Press
- Tseng, L.Y., "A genetic approach to the automatic clustering problem", Pattern Recognition, 34, pp.415~424 (2001)
- Xu, R., "Survey of clustering algorithms", IEEE Transactions on Neural Networks, 16(3), pp.645~678 (2005)