共查询到19条相似文献,搜索用时 963 毫秒
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自组织特征映射网络学习结束后,其输出层结点能够反映出样本空间中数据的分布情况,相似结点代表的数据也相对近似,因而通过对输出层结点的合理划分能够实现对样本空间的有效聚类,结点的划分仍然存在聚类数目不确定的问题,凝聚层次聚类每一层都代表一种聚类结果,找出结果最好的那一层所具有的簇的数目作为最佳聚类数目,是一种比较好的确定聚类数目的方法,在Iris数据集上的实验证明了该方法的有效性. 相似文献
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针对已有的无线入侵检测方法训练时间长和检测精度低的问题,提出一种基于调整后的BIRCH——MBIRCH算法的无线Mesh网络入侵检测算法。该算法首先一次性扫描数据集获得CF(聚类特征),然后自底向上地计算不同层次的聚类有效指标,主要是考虑数据集的几何结构,即通过度量簇内数据点分布的紧凑度以及簇间的相似度,并保持二者之间的平衡,根据此指标确定CF树的簇结点,直到得到最佳聚类结果,将最佳聚类结果作为训练样本指定判别函数,对网络数据定位。实验结果表明,该算法不仅明显减少样本训练时间,同时提高了算法检测精度,符合无线Mesh网络的入侵检测需要。 相似文献
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模糊C-均值(FCM)聚类算法的一个主要问题是需要事先确定聚类的数目,为此定义了类内差异度和类间重叠度来分别度量同一个聚类中数据的相似度和不同聚类间的分离程度,进而基于这两个度量提出一个新的有效性函数用于判定最佳聚类数目。实验结果表明,该有效性函数能有效地判定聚类数目,并且有较好的鲁棒性。 相似文献
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与经典的K均值聚类算法相比,模糊C均值(FCM)聚类算法通过引入模糊因子,考虑不同聚类数据簇之间的相互关系,得到可分性更好的聚类结果。但是模糊因子的引入,使得任意一个样本点都存在模糊性,造成FCM极易受到噪声和离群点的影响,聚类结果泛化性能较差。因此,该文提出一种簇间可分的鲁棒FCM算法(RBI-FCM)。RBI-FCM利用K均值算法对模糊隶属度的稀疏特征,降低不同数据簇之间的相互作用,突出不同数据簇相邻区域的可分性;另外,RBI-FCM在极小化数据簇内部散布度的条件下,考虑不同数据簇之间的可分性,可提高聚类模型的泛化性能。该文设计了有效的模型求解迭代算法。实验结果表明,RBI-FCM算法提高了FCM的鲁棒性,有效降低FCM对数据簇分布差异性和抽样不均衡的敏感性,得到理想的聚类结果。 相似文献
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传统的聚类算法用在MQAM信号的调制识别中,算法的迭代次数多,特别对高阶调制信号运算时间长。针对该问题,提出了一种改进的半监督聚类重构星座图的方法,用标记的样本点来指导隶属度和聚类中心的更新,降低了算法的运算复杂度,减少了迭代次数,聚类中心数目准确。通过分析接收端星座图,提取星座图的特征参数R并与标准星座图的参数Rs进行比较,实现了MQAM信号调制方式的识别。仿真结果表明该方法对MQAM信号的识别率在90%之上,且算法的复杂度低,尤其当调制阶数较高、数据长度较长时,能够将运算时间减少为原来的1/3。 相似文献
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文中提出了一种新的基于数据局部和全局分布特性的K--Means初始化方法.算法通过对数据空间进行网格化后统计每个网格中数据点数目,选取具有数目局部最大值的网格,再利用距离优化方法全局的估算出K个初始聚类中心.在人工和真实数据集上,进行了与传统的聚类中心初始化算法的比较.实验结果表明,该算法利用局部最大值网格和距离优化的方法估算的聚类中心能够在保持及改善聚类效果的同时,明显减少迭代次数,提高收敛速度. 相似文献
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面向Weblog的协同聚类算法具有同时发现用户聚类及与之对应的页面聚类的能力,已成为Weblog数据挖掘的重要研究内容。由于现有的面向Weblog的协同聚类算法大多采用硬划分方法将用户和页面分配到聚类,因此,无法很好地处理聚类边界的问题,即一个用户可能属于多个聚类,从而影响了聚类质量。该文给出了一种面向Weblog的模糊协同聚类FCOW(Fuzzy CO-clustering for Weblog)算法来解决协同聚类算法的边界问题,以提高聚类结果的质量。该算法首先利用矩阵Hadamard积运算发现Weblog中隐含的独立用户模式1={,,K}PA pa pa;其次,依据pa k所对应的页面子集将剩余用户分配到该独立模式中,从而产生协同聚类结果 {k,k}CS CP,k=1,,K;最后计算每个用户和页面与协同聚类之间的模糊隶属度,并以该隶属度作为个性化推荐的依据。实验结果表明,FCOW算法具有获得高质量聚类结果的能力。 相似文献
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介绍了一种代替传统轧机液压缸行程测量的新方法,并以一款PIC24单片芯片为例,配合涡街流量计,详细描述了系统的工作原理、主要特点和软硬件设计,总结了现场实际使用情况。 相似文献
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For many clustering algorithms, it is very important to determine an appropriate number of clusters, which is called cluster validity problem. In this paper, a new clustering validity assessment index is proposed based on a novel method to select the margin point between two clusters for inter-cluster similarity more accurately, and provides an improved scatter function for intra-cluster similarity. Simulation results show the effectiveness of the proposed index on the data sets under consideration regardless of the choice of a clustering algorithm. 相似文献
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Shih-Ming Pan Kuo-Sheng Cheng 《IEEE transactions on systems, man and cybernetics. Part C, Applications and reviews》2007,37(5):827-838
Traditional clustering algorithms (e.g., the K-means algorithm and its variants) are used only for a fixed number of clusters. However, in many clustering applications, the actual number of clusters is unknown beforehand. The general solution to this type of a clustering problem is that one selects or defines a cluster validity index and performs a traditional clustering algorithm for all possible numbers of clusters in sequence to find the clustering with the best cluster validity. This is tedious and time-consuming work. To easily and effectively determine the optimal number of clusters and, at the same time, construct the clusters with good validity, we propose a framework of automatic clustering algorithms (called ETSAs) that do not require users to give each possible value of required parameters (including the number of clusters). ETSAs treat the number of clusters as a variable, and evolve it to an optimal number. Through experiments conducted on nine test data sets, we compared the ETSA with five traditional clustering algorithms. We demonstrate the superiority of the ETSA in finding the correct number of clusters while constructing clusters with good validity. 相似文献
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To solve the problem of mixing matrix estimation for underdetermined blind source separation (UBSS) when thenumber of sources is unknown, this paper proposed a novel mixing matrix estimation method based on averageinformation entropy and cluster validity index (CVI). Firstly, the initial cluster center is selected by using fuzzy C-means (FCM) algorithm and the corresponding membership matrix is obtained, and then the number of clusters isobtained by using the joint decision of CVI and average information entropy index of membership matrix, thenmultiple cluster number estimation results can be obtained by using multiple CVIs. Then, according to the results ofthe number of multiple clusters estimation, the number of radiation sources is determined according to the principleof the subordination of the minority to the majority. The cluster center vectors obtained from the clustering operationof the estimated number of radiation sources are fused, that is the mixing matrix is estimated based on the degree ofsimilarity of the cluster center vectors. When the source signal is not sufficiently sparse, the time-frequency singlesource detection processing can be combined with the proposed method to estimate the mixing matrix. Theeffectiveness of the proposed method is validated by experiments. 相似文献
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利用PBM模糊聚类有效性函数以图像特征空间为搜索空间,实现有效性函数的全局寻优,用并行小生境技术解决粒子群(PSO)算法的早收敛问题,优化聚类的全局收敛性能,实现有效聚类数目与聚类中心的并行寻优。通过对遥感图像分割的实验证明,与传统粒子优化群算法的分割结果相比,本文算法拥有更高的有效性且分割效果更优。 相似文献
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基于模糊聚类和遗传算法的具备解释性和 精确性的模糊分类系统设计 总被引:3,自引:0,他引:3
提出一种基于模糊聚类和遗传算法的模糊分类系统的设计方法.首先定义了模糊分类系统的精确性指标,给出解释性的必要条件.然后利用聚类有效性分析确定模糊规则数目,利用模糊聚类算法辨识初始的模糊分类系统.随后利用模糊集合相似性分析与融合对初始的模糊分类系统进行约简,提高其解释性;利用遗传算法对约简后的模糊分类系统进行优化,提高其精确性,该过程反复迭代直至满足中止条件.最后利用该方法进行Iris数据样本分类,仿真结果验证了该方法的有效性. 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