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针对传统模糊聚类算法对初始聚类中心非常敏感以及对高光谱图像处理效果不佳的问题,为减少聚类数据的复杂度、降低聚类过程的计算成本以提升聚类性能,提出了一种基于多层二部图的高光谱模糊聚类算法。首先使用SuperPCA预处理方法对超像素分割得到的每个同质区域进行PCA来学习HSI数据不同区域的固有低维特征,从而获得高光谱数据的低维表示;其次,构造一个多层二部图矩阵来描述数据点和锚点之间的关系,降低了计算复杂度;最后,在模糊聚类中加入基于多层二部图的非负正则项来约束模糊隶属度矩阵的解空间。在Indian Pines和Pavia University数据集上进行的实验表明,所提算法能提高聚类效果与性能。 相似文献
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Xiaolu Zhang 《International journal of systems science》2013,44(3):562-576
Recently, hesitant fuzzy sets (HFSs) have been studied by many researchers as a powerful tool to describe and deal with uncertain data, but relatively, very few studies focus on the clustering analysis of HFSs. In this paper, we propose a novel hesitant fuzzy agglomerative hierarchical clustering algorithm for HFSs. The algorithm considers each of the given HFSs as a unique cluster in the first stage, and then compares each pair of the HFSs by utilising the weighted Hamming distance or the weighted Euclidean distance. The two clusters with smaller distance are jointed. The procedure is then repeated time and again until the desirable number of clusters is achieved. Moreover, we extend the algorithm to cluster the interval-valued hesitant fuzzy sets, and finally illustrate the effectiveness of our clustering algorithms by experimental results. 相似文献
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传统的模糊方法已无法解决数据本身不确定性的问题,犹豫模糊集方法却行之有效.原有的犹豫模糊层次聚类算法没有考虑犹豫模糊集对权值的影响,缺乏合理的权重计算方法,并且算法的时间复杂度和空间复杂度都为指数级.为了更有效地解决聚类分析问题,本文提出一种凝聚中心犹豫度恒定的模糊层次聚类算法(FHCA),首先设计了一种基于数据集本身... 相似文献
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Dealing with uncertainty is an undeniable challenge in the real‐world problems. In this paper, we focus on hesitant environment such as generalized hesitant fuzzy sets introduced by Qian et al. So we propose a new generalized hesitant fuzzy hierarchical clustering (GHFHC) algorithm based on Atanassov's intuitionistic fuzzy set theory. We extend conventional hierarchical clustering, which just works on the crisp data, and introduce a clustering algorithm, which can be applied on large data set with generalized hesitant fuzzy data. The run time of the GHFHC algorithm shows that its computational complexity will be low. Also, the GHFHC algorithm produces the clusters with arbitrary shapes by using the various distance measures. Finally, an example is provided to illustrate the practicality of the proposed algorithm. 相似文献
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针对复杂非线性数据的无监督学习问题,提出一种新型的映射方式来有效提高算法对复杂非线性数据的学习能力。以TSK模糊系统的规则前件学习为基础,提出一种新型的模糊特征映射新方法。接着,针对映射之后的数据维度过大问题,引入多层递阶融合的概念,进一步提出基于多层递阶融合的模糊特征映射新方法,从而有效避免了因单层模糊特征映射之后特征维数过高而导致的数据混乱和冗余的问题。最后与模糊C均值算法相结合,提出基于多层递阶融合模糊特征映射的模糊C均值聚类算法。实验研究表明,文中算法相比于经典模糊聚类方法,有着更加优越、稳定的性能。 相似文献
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An Improved Clustering Algorithm Using Fuzzy Relation for the Performance Evaluation of Humanistic Systems
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A hierarchical structure is proposed for the performance evaluation of vague, complicated humanistic systems. An improved fuzzy clustering algorithm is developed to produce several partition trees with different levels and clusters according to different triangular norm compositions. Additionally, a fuzzy clustering algorithm is given to produce a partition tree without using the transitive closure composition. The usefulness of the proposed algorithm is illustrated by an example of actual academic data. 相似文献
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对基于区间值数据的模糊聚类算法进行了研究,介绍了具有控制区间大小对聚类结果影响的加权因子的模糊C-均值聚类新算法.针对区间值数据模糊C-均值聚类新算法提出了一个适应距离的弹性系数,使算法得到改进,既能利用传统的FCM算法,又考虑了区间大小对聚类结果的影响,同时也能发现不规则的聚类子集,使聚类结果更加准确. 相似文献
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In this paper, a multistage evolutionary scheme is proposed for clustering in a large data base, like speech data. This is achieved by clustering a small subset of the entire sample set in each stage and treating the cluster centroids so obtained as samples, together with another subset of samples not considered previously, as input data to the next stage. This is continued till the whole sample set is exhausted. The clustering is accomplished by constructing a fuzzy similarity matrix and using the fuzzy techniques proposed here. The technique is illustrated by an efficient scheme for voiced-unvoiced-silence classification of speech. 相似文献
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针对DBN算法训练时间复杂度高,容易过拟合等问题,受模糊理论启发,提出了一种基于模糊划分和模糊加权的集成深度信念网络,即FE-DBN(ensemble deep belief network with fuzzy partition and fuzzy weighting),用于处理大样本数据的分类问题。通过模糊聚类算法FCM将训练数据划分为多个子集,在各个子集上并行训练不同结构的DBN,将每个分类器的结果进行模糊加权。在人工数据集、UCI数据集上的实验结果表明,提出的FE-DBN比DBN精度均有所提升,具有更快的运行时间。 相似文献
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经典的模糊C-均值聚类算法存在对噪声数据较为敏感、未考虑样本属性特征间的不平衡性及对高维数据聚类不理想等问题,而可能性聚类算法虽然解决了噪声敏感和一致性聚类问题,但算法假定每个样本对聚类的贡献程度一样。针对以上问题,提出了一种基于样本-特征加权的可能性模糊核聚类算法,将可能性聚类应用到模糊聚类中以提高其对噪声或例外点的抗干扰能力;同时,根据不同类的具体特性动态计算样本各个属性特征对不同类别的重要性权值及各个样本对聚类的重要性权值,并优化选取核参数,不断修正核函数把原始空间中非线性可分的数据集映射到高维空间中的可分数据集。实验结果表明,基于样本-特征加权模糊聚类算法能够减少噪声数据和例外点的影响,比传统的聚类算法具有更好的聚类准确率。 相似文献
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This article presents PFCM, a parallel algorithm for fuzzy clustering of large data sets. Being a generalization of FCM, the algorithm enables arbitrary numbers of data points, features and clusters to be handled cost-optimally by hypercube SIMD computers of arbitrary cube dimension, the only limitation being the size of the local memories of the processors. Speedup responds optimally to enlarging the hypercube. PFCM owes its flexibility to the technique employed in its derivation from the sequential fuzzy C-means algorithm FCM: the association of each of the three dimensions of the problem (numbers of data points, features and clusters) with a distinct subset of hypercube dimensions. 相似文献
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E. Lefever Author Vitae T. Fayruzov Author Vitae M. De Cock Author Vitae 《Information Sciences》2010,180(17):3192-4672
Person name queries often bring up web pages that correspond to individuals sharing the same name. The Web People Search (WePS) task consists of organizing search results for ambiguous person name queries into meaningful clusters, with each cluster referring to one individual. This paper presents a fuzzy ant based clustering approach for this multi-document person name disambiguation problem. The main advantage of fuzzy ant based clustering, a technique inspired by the behavior of ants clustering dead nestmates into piles, is that no specification of the number of output clusters is required. This makes the algorithm very well suited for the Web Person Disambiguation task, where we do not know in advance how many individuals each person name refers to. We compare our results with state-of-the-art partitional and hierarchical clustering approaches (k-means and Agnes) and demonstrate favorable results. This is particularly interesting as the latter involve manual setting of a similarity threshold, or estimating the number of clusters in advance, while the fuzzy ant based clustering algorithm does not. 相似文献
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为了实现Web服务请求数据的快速聚类,并提高聚类的准确率,提出一种基于增量式时间序列和任务调度的Web数据聚类算法,该算法进行了Web数据在时间序列上的聚类定义,并采用增量式时间序列聚类方法,通过数据压缩的形式降低Web数据的复杂性,进行基于服务时间相似性的时间序列数据聚类。针对Web集群服务的最佳服务任务调度问题,通过以服务器执行能力为标准来分配服务任务。实验仿真结果表明,相比基于网格的高维数据层次聚类算法和基于增量学习的多目标模糊聚类算法,提出的算法在聚类时间、聚类精度、服务执行成功率上均获得了更好的效果。 相似文献
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《Fuzzy Systems, IEEE Transactions on》2009,17(1):243-252
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The conventional approach of terrain image classification that assigns a specific class for each pixel is inadequate, because the area covered by each pixel may embrace more than a single class. Fuzzy set theory which has been developed to deal with imprecise information can be incorporate in the analysis for a more appropriate solution to this problem. In the current state of imaging radar technology, polarimetric synthetic aperture radar (SAR) is unique in providing complete polarization information of ground covers for more effective classification than a single polarization radar. In this paper, we use the fuzzy c-means clustering algorithm for unsupervised segmentation of multi-look polarimetric SAR images. A statistical distance measure adopted in this algorithm is derived from the complex Wishart distribution of the complex covariance matrix. In classifying polarimetric SAR imagery, each terrain class is characterized by its own feature covariance matrix. The algorithm searches for cluster centres for each class and generates a fuzzy partition for the whole image. Membership grades obtained for each pixel provide detailed information about spatial terrain variations. Classification of the image is achieved by choosing a defuzzification criterion. When the back-scattering characteristics of two or more classes are not well distinguished from each other, a divisive hierarchical clustering procedure is adopted to locate their respective feature covariance matrices. NASA/JPL AIRSAR data is used to substantiate this fuzzy classification algorithm. 相似文献
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基于分级神经网络的Web文档模糊聚类技术 总被引:2,自引:1,他引:1
给出了一种多层向量空间模型,该模型将一篇文档的相关信息从逻辑上划分为多个相对独立的文本段,按照不同位置的文本段确定相应的索引项权重.然后提出了一种简明而有效的基于分级神经网络的模糊聚类算法.与现有方法不同,该模糊聚类方法采用自组织神经网络和模糊聚类网络两部分组成的3层神经网络来实现.首先采用自组织神经网络从原始数据产生一个初始聚类结果,然后运用FCM方法对初始聚类的数目进行优化.实验结果表明,提出的Web文档聚类算法具有较好的聚类特性,它能将与一个主题相关的web文档较完全和准确地聚成一类. 相似文献
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提出了一种基于新相异度量的模糊K-Modes算法。该算法假定不同属性对聚类结果有不同程度的影响,定义了新的属性值函数,以基于划分相似度的聚类精确度作为聚类结果的评价准则。通过真实数据的实验结果表明,新的基于相异度量的模糊K-Modes算法比传统的模糊K-Modes算法有更好的聚类效果。 相似文献