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聚类分析是将集合中的数据按其相似性大小分成不同类别的一种方法,它是模式
识别中多变量无监督学习的一个分支,己成功地用于医学,地质,财务,工程,图像
处理和文档等的数据分类中;含有实现此算法的源码
-cluster analysis is to pool the data according to similar size into a different category, It is pattern recognition multivariable Unsupervised Le
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A fault identification with fuzzy C-Mean clustering
algorithm based on improved ant colony algorithm (ACA) is
presented to avoid local optimization in iterative process of
fuzzy C-Mean (FCM) clustering algorithm and the difficulty in
fault cl
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基于聚类分析的脑电信号数据处理,详细讲了脑电的基础知识然后讲了具体的脑电采集,然后是分类-EEG data processing based on cluster analysis, saying more about the basics of the EEG and then talk about specific EEG acquisition and classification
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由于K-means 聚类方法对遥感图像进行分类时,对训练样本的选取依赖性很大,容易陷入局部最优的陷阱的情况,本文提出利用模拟退化算法对K-means 的聚类进行优化以获得
全局最优解的分类新方案。并以多波段影像为例进行验证分析,结果表明该方法可行,收敛
结果优于K-means 聚类算法,分类精度相对传统的K-means 算法更高。-Because K-means clustering classification depend on the training sample selecti
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对现有数字调制方式识别类型有限的问题,提出一种基于星座图的分类算法。算法首先利用盲均衡技术克服信道的多径效应与系统问步误差,再对信号减法聚类,提取聚类中心与理想星座图模型进行匹配,从而实现MAsK、MPsK、MOAM等调制方式的识别。-Existing types of digital modulation recognition of limited problem, a classification algorithm based on the constellation. Firstly,
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The primary goal of pattern recognition is supervised or unsupervised classification. Among the various frameworks in
which pattern recognition has been traditionally formulated, the statistical approach has been most intensively studied and used i
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Timbre is described as the tone color of a sound which helps to distinguish between different sounds.For a single musical instrument sound the timbre can be classified into different categories using K-means cluster analysis.
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通过对K-均值算法的编程实现,加强对该算法的理解和认识。提高自身的知识水平和编程能力,认识模式识别在生活中的应用。
算法思想K-均值算法的主要思想是先在需要分类的数据中寻找K组数据作为初始聚类中心,然后计算其他数据距离这三个聚类中心的距离,将数据归入与其距离最近的聚类中心,之后再对这K个聚类的数据计算均值,作为新的聚类中心,继续以上步骤,直到新的聚类中心与上一次的聚类中心值相等时结束算法。-By programming K- means algorithm implementation, s
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