A Genetic Algorithms Based Mercer Kernel Clustering Method
ZHOU LinFeng1 , DING YongSheng2, 3
1.College of Computer Sciences and Technology, Donghua University, Shanghai 201620 2.College of Information Sciences and Technology, Donghua University, Shanghai 201620 3.Engineering Research Center of Digitized Textile and Fashion Technology, Donghua University, Shanghai 201620
Abstract Based on Mercer kernel function theorem and genetic algorithms, a novel genetic algorithms based mercer kernel clustering method is proposed. Using Mercer kernel function, an elegant way to map the input space nonlinearly to the high dimensional feature space is gotten, which manifests the feature differences of samples, so that it not only gets more accurate clustering results but also speeds up the convergence rate of the algorithm. And, integrated with genetic algorithm, it can overcome the problem of local minima. Simulation experiments and an application to the objective evaluation of textile products verify its feasibility and effectiveness.
[1] Kanungo T, Mount D M, Netanyahu N S, et al. An Efficient K-Means Clustering Algorithm: Analysis and Implementation. IEEE Trans on Pattern Analysis and Machine Intelligence, 2002, 24(7): 881-892 [2] Hathaway R J, Bezdek J C, Pal N R. Sequential Competitive Learning and the Fuzzy c-Means Clustering Algorithms. Neural Networks, 1996, 9(5): 787-796 [3] Schlkopf B, Mika S, Burges C J C, et al. Input Space vs. Feature Space in Kernel-Based Methods. IEEE Trans on Neural Networks, 1999, 10(5): 1000-1017 [4] Camastra F, Verri A. A Novel Kernel Method for Clustering. IEEE Trans on Pattern Analysis and Machine Intelligence, 2005, 27(5): 801-804 [5] Lee J, Lee D. An Improved Cluster Labeling Method for Support Vector Clustering. IEEE Trans on Pattern Analysis and Machine Intelligence, 2005, 27(3): 461-464 [6] Doval D, Mancoridis S, Mitchell B S. Automatic Clustering of Software Systems Using a Genetic Algorithm. In: Proc of the International Conference on Software Tools and Engineering Practice. Pittsburgh, USA, 1999, 73-91 [7] Schnitman L, Yoneyama T. A Clustering Method for Improving the Global Search Capability of Genetic Algorithms. In: Proc of the 6th IEEE Brazilian Symposium on Neural Networks. Rio de Janeiro, Brazil, 2000, 32-37 [8] Hou C H, Ding Y S, Zhou L F, Zeng X Y. Kernel Clustering Based Method for Fabric Evaluation. In: Proc of the 17th IMACS World Congress on Scientific Computation, Applied Mathematics and Simulation. Paris, France, 2005. http://sab.sscc.ru/imacs2005/papers/T3-I-82-1004.pdf
2006, Vol. 19 
