【摘 要】
：
分析了基于摄动的模糊聚类方法(fuzzy clustering method based on perturbation,FCMBP),指出指数复杂度的遍历过程是目前PC计算环境下难以处理十阶以上较高阶数模糊相似矩阵
【机 构】
：
中国科学院计算技术研究所智能信息处理重点实验室,中国科学院研究生院,湘潭大学信息工程学院计算机系,
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分析了基于摄动的模糊聚类方法(fuzzy clustering method based on perturbation,FCMBP),指出指数复杂度的遍历过程是目前PC计算环境下难以处理十阶以上较高阶数模糊相似矩阵的原因.把寻求具有最小“失真”的最优模糊等价矩阵看作优化问题来求解,提出了一种基于进化规划的FCMBP模糊聚类改进方法.与FCMBP相比,该方法通过引入基于进化规划的优化技术避免了遍历过程,使其能够对高阶模糊相似矩阵进行处理.得到的等价矩阵“失真”小于传递闭包法所得结果,从而获得更为精确可靠的聚类效果,将FCMBP模糊聚类方法推广到能够处理高阶模糊相似矩阵的情形,满足应用需要.
The fuzzy clustering method based on perturbation (FCMBP) is analyzed. It is pointed out that the exponential complexity traversal process is the reason that it is difficult to deal with the higher order fuzzy similar matrix above ten order in PC computing environment. To find the optimal fuzzy equivalent matrix with minimum “distortions ” as an optimization problem, an improved FCMBP fuzzy clustering method based on evolutionary programming is proposed. Compared with FCMBP, The optimization technique avoids the traversal process and makes it possible to process higher-order fuzzy similar matrices. The resulting equivalent matrix “distortion ” is smaller than the result obtained by the transitive closure method to obtain a more accurate and reliable clustering effect. The FCMBP Fuzzy clustering method is extended to the case that can handle high-order fuzzy similar matrix to meet the application needs.
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