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模糊概念的EI代数分解 总被引:4,自引:1,他引:3
应用 AFS结构 [6] 上的 EI代数[6] 分析模糊概念的数学结构 ,证明有限集上任意一个模糊概念都是一类极其简单的模糊概念的 EI代数分解。 相似文献
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首先介绍了模糊聚类分析在分类中的重要性,其次介绍了模糊聚类分析的步骤,最后将模糊聚类分析算法与实际问题结合起来,并给出了分类结果,验证了方法的有效性. 相似文献
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土壤是一个多性状的连续体,其分类的首选方法是模糊聚类分析.但是模糊聚类分析中现有的基于模糊等价关系的动态聚类法和模糊c-均值法各有利弊,采用其中一种方法聚类肯定存在不足.为此集成两种聚类方法的优点,避其缺点,提出了用基于模糊等价关系的动态聚类方法和方差分析方法确定聚类数目和初始聚类中心,再用模糊c-均值法决定最终分类结果的集成算法,并将其应用到松花江流域土壤分类中,得到了较为切合实际的分类结果. 相似文献
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本文成功地应用Fuzzy聚类分析方法对舰船的磁性防护性能等级进行了分类,并且在聚类分析结果的基础上,应用模式识别技术对多个舰船磁场样本的磁性防护性能进行了预测,其结果是令人满意的。该方法克服了文献[2]所提出的模拟对抗计算方法的计算时间长、模型复杂等缺点。 相似文献
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模糊C均值算法的改进 总被引:13,自引:0,他引:13
模糊聚类分析方法具有较强的实用性,但传统的模糊C均值算法对数据集进行分类时有均分的趋势,对于数据集中各类样本数目相差较大的情况,其聚类结果不是很理想.因此,本文对FCM算法进行了改进,使之不但能够达到更好的分类效果,同时也更加适用于样本分类不均衡的聚类问题.文中还结合具体算例进行了聚类分析,得到了理想的分类效果. 相似文献
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聚类分析(Ⅰ) 总被引:5,自引:0,他引:5
方开泰 《数学的实践与认识》1978,(1)
聚类分析(Cluster Analysis)是数理统计中研究“物以类聚”的一种方法,近十年来发展很快,从数值分类学中独立出来成为专门的分枝,并且在地质勘探、天气预报、生物分类、考古学、医学、心理学以及制定国家标准等许多方面都取得了许多很有成效的应用.由于聚类分析的方法简单有效,近年来引起了各方面对它的重视,有关的专著陆续出了几本,如参考资料[3,4]等;有些杂志连载了这个方法的讲座,如[7]等.聚类分析和数理统计传 相似文献
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This paper presents a fuzzy clustering algorithm, called the alternative fuzzy c-numbers (AFCN) clustering algorithm, for LR-type fuzzy numbers based on an exponential-type distance function. On the basis of the gross error sensitivity and influence function, this exponential-type distance is claimed to be robust with respect to noise and outliers. Hence, the AFCN clustering algorithm is more robust than the fuzzy c-numbers (FCN) clustering algorithm presented by Yang and Ko (Fuzzy Sets and Systems 84 (1996) 49). Some numerical experiments were performed to assess the performance of FCN and AFCN. Numerical results clearly indicate AFCN to be superior in performance to FCN. Finally, we apply the FCN and AFCN algorithms to real data. The experimental results show the superiority of AFCN in Taiwanese tea evaluation. 相似文献
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Issam Dagher 《Fuzzy Optimization and Decision Making》2018,17(2):159-176
In this paper, we propose a new kernel-based fuzzy clustering algorithm which tries to find the best clustering results using optimal parameters of each kernel in each cluster. It is known that data with nonlinear relationships can be separated using one of the kernel-based fuzzy clustering methods. Two common fuzzy clustering approaches are: clustering with a single kernel and clustering with multiple kernels. While clustering with a single kernel doesn’t work well with “multiple-density” clusters, multiple kernel-based fuzzy clustering tries to find an optimal linear weighted combination of kernels with initial fixed (not necessarily the best) parameters. Our algorithm is an extension of the single kernel-based fuzzy c-means and the multiple kernel-based fuzzy clustering algorithms. In this algorithm, there is no need to give “good” parameters of each kernel and no need to give an initial “good” number of kernels. Every cluster will be characterized by a Gaussian kernel with optimal parameters. In order to show its effective clustering performance, we have compared it to other similar clustering algorithms using different databases and different clustering validity measures. 相似文献
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Clustering algorithms divide up a dataset into a set of classes/clusters, where similar data objects are assigned to the same
cluster. When the boundary between clusters is ill defined, which yields situations where the same data object belongs to
more than one class, the notion of fuzzy clustering becomes relevant. In this course, each datum belongs to a given class
with some membership grade, between 0 and 1. The most prominent fuzzy clustering algorithm is the fuzzy c-means introduced
by Bezdek (Pattern recognition with fuzzy objective function algorithms, 1981), a fuzzification of the k-means or ISODATA
algorithm. On the other hand, several research issues have been raised regarding both the objective function to be minimized
and the optimization constraints, which help to identify proper cluster shape (Jain et al., ACM Computing Survey 31(3):264–323,
1999). This paper addresses the issue of clustering by evaluating the distance of fuzzy sets in a feature space. Especially,
the fuzzy clustering optimization problem is reformulated when the distance is rather given in terms of divergence distance,
which builds a bridge to the notion of probabilistic distance. This leads to a modified fuzzy clustering, which implicitly
involves the variance–covariance of input terms. The solution of the underlying optimization problem in terms of optimal solution
is determined while the existence and uniqueness of the solution are demonstrated. The performances of the algorithm are assessed
through two numerical applications. The former involves clustering of Gaussian membership functions and the latter tackles
the well-known Iris dataset. Comparisons with standard fuzzy c-means (FCM) are evaluated and discussed. 相似文献
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将粗糙集理论与模糊集理论结合起来,给出一种连续值域决策表的离散化算法。该算法从已知数据的初始决策系统出发,首先构造对像的相似矩阵,然后根据相似矩阵的传递闭包及粗糙集正域的思想得出决策表的条件类,再根据条件类将连续值决策表化为区间值决策表,最后根据各区间值将连续值域决策表化为离散决策表。 相似文献
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本文主要方法是通过基本序列、导出拟阵序列和模糊集分解定理,将模糊圈的研究转化为对圈子集套和数组的研究。在闭模糊拟阵中,我们得出三个结论:以同一集合为支撑集的模糊圈的最大模糊圈总是存在;以同一子集串为圈子集套的模糊圈的最大模糊圈不一定存在。但是,找到了存在最大模糊圈的充要条件;以同一集合为支撑集的模糊圈的最小模糊圈,以同一子集串为圈子集套的模糊圈的最小模糊圈都是不存在的。但它们的最小模糊势是存在的,而且找出了计算最小模糊势的公式。我们构造了两个算法:一是构造支撑集最大模糊圈算法。通过这个算法可构造出支撑集最大模糊圈,同时计算出其最大模糊势;二是判断和构造圈子集套最大模糊圈算法。通过这个算法首先判断最大模糊圈是否存在,如果存在就可以找出圈子集套最大模糊圈同时计算出最大模糊势。 相似文献