共查询到19条相似文献,搜索用时 140 毫秒
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针对传统的 K-Means 聚类雷达信号分选算法对初始聚类中心敏感和易陷入局部最优解的缺点,将改进的人工蜂群算法和 K-Means 迭代相结合,提出了一种混合聚类雷达信号分选算法,使算法对初始聚类中心的依赖性和陷入局部最优解的可能性降低,提高了算法的稳定性。通过仿真实验证明该算法分选准确率高,为雷达信号分选提供了新的思路。 相似文献
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K-Means算法在雷达信号预分选中有着广泛的应用,传统K-Means算法对聚类个数以及聚类中心的初始设定依赖性很大,并且对噪声和孤立点很敏感,针对这些不足,文中提出了一种将距离法与改进的K-Means算法相结合的雷达信号预分选方法。仿真实验表明提出的方法可以有效的降低了噪声和孤立点对K-Means聚类算法的影响。 相似文献
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为了解决聚类算法需要较多的先验知识,不能自动进行聚类的问题,提出了基因表达式编程和K-Means融合的雷达信号分选算法。从介绍基因表达式编程和K-Means聚类算法的特点出发,针对雷达信号的实际情况,对两种算法进行了优化融合,并通过模拟雷达辐射源数据进行了仿真验证,仿真结果表明该算法在不需要任何雷达辐射源先验知识的情况下即可自动完成聚类分选,具有98.3%的聚类分选精度和较快的收敛速度,其较高的分选精度在电子情报侦察系统上有着广阔的应用前景。 相似文献
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大多数盲信号分离算法只适用于非高斯、平稳、相互独立的源信号,因此使用高阶统计量(HOS)有效解决以上问题。然而非平稳环境在实际问题中经常出现,本文主要考虑基于Cohen类时-频分布的Wigner—Viue分布的有噪声污染的盲信号分离算法。通过联合对角化一组空间时-频分布矩阵,给出了一种选取时频点初值的选取准则,最后给出了针对非平稳混合信号的盲分离算法。仿真实验说明本文提出算法的有效性。 相似文献
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<正>为了缓解交通拥堵,提高出行效率,交通部门需要对交通流状态进行分类识别以确定交通状态。基于K-Means聚类算法进行公路运行状态划分易受到初始聚类中心点选择的影响,因此本文在K-Means算法的基础上进行改进,将BisectingK-Means应用于公路运行状态的划分,各交通状态中心点的距离较远,避免了初始聚类中心会聚到一个交通状态,一定程度上克服了K-Means算法陷入局部最优值。 相似文献
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Cluster analysis of gene expression data based on self-splitting and merging competitive learning 总被引:8,自引:0,他引:8
Shuanhu Wu Liew A.W.-C. Hong Yan Mengsu Yang 《IEEE transactions on information technology in biomedicine》2004,8(1):5-15
Cluster analysis of gene expression data from a cDNA microarray is useful for identifying biologically relevant groups of genes. However, finding the natural clusters in the data and estimating the correct number of clusters are still two largely unsolved problems. In this paper, we propose a new clustering framework that is able to address both these problems. By using the one-prototype-take-one-cluster (OPTOC) competitive learning paradigm, the proposed algorithm can find natural clusters in the input data, and the clustering solution is not sensitive to initialization. In order to estimate the number of distinct clusters in the data, we propose a cluster splitting and merging strategy. We have applied the new algorithm to simulated gene expression data for which the correct distribution of genes over clusters is known a priori. The results show that the proposed algorithm can find natural clusters and give the correct number of clusters. The algorithm has also been tested on real gene expression changes during yeast cell cycle, for which the fundamental patterns of gene expression and assignment of genes to clusters are well understood from numerous previous studies. Comparative studies with several clustering algorithms illustrate the effectiveness of our method. 相似文献
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一种基于调和均值的模糊聚类算法 总被引:1,自引:0,他引:1
k调和均值算法用数据点与所有聚类中心的距离的调和平均替代了数据点与聚类中心的最小距离,是一种减小初始值影响聚类结果的有效的聚类方法。本文对k调和均值算法进行扩展,考虑到数据点同时对不同聚类的隶属关系,将模糊的概念应用到聚类中,提出了模糊k调和均值-Fuzzv K—Harmonic Means(FKHM)算法。在中心迭代聚类算法的统一框架的基础上,推导出FKHM算法聚类中心的条件概率表达式以及在迭代过程中的数据点加权函数表达式。以划分相似度作为聚类结果的评价准则,实验表明,FKHM算法在聚类对于初值不敏感的同时提高了聚类结果的精确度,达到较好的聚类效果。 相似文献
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提出一种适用于道路障碍物识别检测的聚类算法,该算法用来处理各向异性分布的激光点云数据。算法的基本思想是:针对点云空间分布的实时变化,提出在线学习合并阈值的层次聚类算法,以确定聚类数搜索范围上界和初始聚类中心的待选点集;然后提出距离乘积最大化方法,对待选点集进行初始化排序,既结合点云的空间密度分布改善了聚类结果,又克服了传统K-means算法初始聚类中心难确定的问题;最后选取Silhouette和距离评价函数为聚类有效性指标分析算法的聚类效果,确定最佳聚类数。用以上自适应、在线学习的算法对2.5D激光雷达采集的点云数据进行聚类,并与其他两种聚类算法进行实际试验比较发现,本算法可以正确分割大多数空间分布各异且相互连接的障碍物。 相似文献
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传统的用于Web日志聚类的算法大都需要用户指定聚类个数。提出了一种新的自适应聚类算法并对Web日志用户会话进行聚类。该算法基于凝聚聚类思想和划分聚类思想,用初始数据集中每2个会话之间的相异度作为距离的度量,合并距离小于一定阈值的两个会话以产生初始聚类,再根据一定的规则动态地合并距离最小的会话类或会话,算法的结果是产生自然的聚类。最后,通过比较会话聚类的内部距离和类间距离来验证算法的有效性。这种聚类算法的最大优点在于,他能够产生自动的聚类,而不需要用户事先指定需要产生的聚类个数,并且能有效识别孤立点。实验表明,这种聚类能够产生较高质量的聚类效果。 相似文献
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Most hyper‐ellipsoidal clustering (HEC) approaches use the Mahalanobis distance as a distance metric. It has been proven that HEC, under this condition, cannot be realized since the cost function of partitional clustering is a constant. We demonstrate that HEC with a modified Gaussian kernel metric can be interpreted as a problem of finding condensed ellipsoidal clusters (with respect to the volumes and densities of the clusters) and propose a practical HEC algorithm that is able to efficiently handle clusters that are ellipsoidal in shape and that are of different size and density. We then try to refine the HEC algorithm by utilizing ellipsoids defined on the kernel feature space to deal with more complex‐shaped clusters. The proposed methods lead to a significant improvement in the clustering results over K‐means algorithm, fuzzy C‐means algorithm, GMM‐EM algorithm, and HEC algorithm based on minimum‐volume ellipsoids using Mahalanobis distance. 相似文献
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Every camera sensor leaves unique traces on the acquired images that can be thought of as a camera fingerprint. This work presents an efficient algorithm for clustering images based on their camera fingerprints. The algorithm performs a fast preliminary clustering based on a compressed representation of the camera fingerprints, then it refines the initial clusters using full-size fingerprints. The efficiency of the method is further improved by scanning the images according to a ranking index that depends on fingerprint estimation quality. The results confirm that the proposed method achieves a performance comparable to the state of the art approaches, with a significantly lower computational complexity, especially on large datasets. The method can also handle cases in which the number of clusters is much larger than the average size of the clusters. 相似文献
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Michihiro Shintani Takumi Uezono Kazumi Hatayama Kazuya Masu Takashi Sato 《Journal of Electronic Testing》2016,32(5):601-609
In this study, a novel path clustering technique for adaptive path delay testing, where the test paths are altered according to the extracted device parameters, is proposed. The proposed algorithm is based on the k-means++ algorithm. By considering the probability function of the die-to-die systematic process variation, the proposed algorithm clusters path sets to minimize the total number of test paths. A figure of merit for clustering, which represents the expected number of test paths, is also proposed for quantitatively evaluating path clustering under different conditions. The proposed clustering method is evaluated numerically by applying it to the OpenCores benchmark circuit. Using our clustering technique, the average number of test paths in the adaptive test is reduced to less than 92 % compared with those in the conventional test. In addition, adaptive testing using the proposed technique can reduce the test patterns by 94.26 % while retaining the test quality. 相似文献