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872 JOURNAL OF SOFTWARE, VOL. 6, NO. 5, MAY 2011 Modeling And Validation,” Vehicle Systems Modeling and Testing, Vol. 1, Nos. 1/2/3, 2005 [14] Tingvall C, Krafft M, Kullgren A, Lie A. “The effectiveness of ESP (electronic stability program) in reducing real-life accidents,” Traffic Injury Prevention, Vol. 5, pp. 37–41, 2004 [15] Najm W, Sen B, Smith J, Campbell B. “Analysis of light vehicle crashes and pre-crash scenarios based on the 2000 General Estimates System,” Report no. DOT-HS-809-573. U.S. Department of Transportation, Washington, DC, 2003 [16] Brown, T. A. et al., “Rollover Stability Control for an Automotive Vehicle, ” US patent No. 6,263,261 B1 [17] Van Zanten, A. T., “Bosch ESP systems: 5 years of experience, ” SAE 2000-01-1633 [18] Branicky, M.S., “Studies in Hybrid Systems: Modeling, Analysis, and Control,” PhD Thesis, Laboratory for Information and Decision Systems, MIT, Cambridge, USA, 1995 © 2011 ACADEMY PUBLISHER [19] Fennel, Ding, ‘A Model-Based Failsafe System for the Continental Teves Electronic Stability Program (ESP)’, SAE 2000-01-1635 Li Shengqin graduated from Northeast Forest University (NEFU), Harbin, Heilongjiang, P.R. China, in 208, and received her Doctor Degree in Vehicle Application Engineering. She is currently an associate professor in the Traffic College, Northeast Forest University, China, and engages in the postdoctoral research in the State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing, China. Her research interest covers vehicle dynamics and control.
JOURNAL OF SOFTWARE, VOL. 6, NO. 5, MAY 2011 873 An Improved Fuzzy C-means Clustering Algorithm based on PSO Abstract—To deal with the problem of premature convergence of the fuzzy c-means clustering algorithm based on particle swarm optimization, which is sensitive to noise and less effective when handling the data set that dimensions greater than the number of samples, a novel fuzzy c-means clustering method based on the enhanced Particle Swarm Optimization algorithm is presented. Firstly, this approach distributes the memberships on the basis of the distance between the sample and cluster centers, making memberships meet the constraints of FCM. Then, optimization strategy is presented that the optimal particle can be guided to close the group effectively. The experimental results show the proposed method significantly improves the clustering effect of the PSO-based FCM that encoded in membership. Index Terms—clustering, particle swarm algorithm, fuzzy C means, membership, constraint strategy I. INTRODUCTION Generally speaking, clustering is in accordance with certain requirements and rules to distinguish between things, and classification of the process. Clustering algorithm is a set of classification of the data that distribution is unknown, the aim is to find the structure hidden in data, and as much as possible to make the data that have same nature attributed to the same class according to some measure of similarity degree. Clustering is a form of unsupervised learning whereby objects that similar to each other are put into the same cluster. It is the first stage of knowledge acquisition concerning a group of objects that is obtaining knowledge of classes. Fuzzy clustering methods that based on the objective function is the most studied in the literature and the most widely used in practice, such algorithm takes the clustering problem as a constrained optimization problem, by solving the optimization problem to determine the fuzzy partition and the clustering results in data set. Such algorithms are characterized by simple and easy to apply © 2011 ACADEMY PUBLISHER doi:10.4304/jsw.6.5.873-879 Qiang Niu School of Computer Science &Technology China University of Mining & Technology Xuzhou Jiangsu, China niuqiang@vip.163.com Xinjian Huang School of Computer Science &Technology China University of Mining & Technology Xuzhou Jiangsu, China xinjian1020@yahoo.cn and clustering performance is good, can take use of the classical optimization theory as its theoretical support, and easy for the programming. Fuzzy c-means clustering algorithm (FCM) [1-2] is an effective algorithm and is one of the most used clustering methods. But when the data set has a higher dimension, the clustering effect of FCM is poor, and it is difficult to find the global optimum [3-4]. Particle Swarm Optimization (PSO) [11] is one of the modern heuristic algorithms under the evolutionary algorithms, and has proved to be very effective for solving global optimization, and gained lots of attention in various engineering applications. It is not only a recently invented high-performance optimizer that is easy to understand and implement, but it also requires little computational bookkeeping and generally only a few lines of code. It is a stochastic search technique with reduced memory requirement, computationally effective and easier to implement compared to other evolutionary algorithms. Clustering problems can be attributed to optimization problems under certain conditions, Particle Swarm Optimization is an optimization algorithm based on the theory of swarm intelligence, which could be implemented and applied easily to solve various function optimization problems, or the problems that can be transformed to function optimization problems. PSO is easy to describe and implement, it also has a strong global search capability and a faster convergence [12]. Many PSO-based Fuzzy Clustering Algorithms are proposed [5-9]. However, in most of these algorithms the particle is encoded by cluster centers, less of these algorithms use the method that the particle is encoded by membership. If a data set has n samples and c clusters, each sample has d dimensions. While encoded by membership, a particle is an one-dimensional row vector with n×c rows. While encoded by cluster centers, a particle is an onedimensional row vector with c×d rows. In a data set, d is
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Aims and Scope. Call for Papers and
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