通过借鉴生物免疫系统中的克隆选择原理和记忆机制，提出了一种基于人工免疫原理的混合聚类算法。
Inspired by the clone selection principle and memory mechanism of the vertebrate immune system, a new hybrid clustering method based on the artificial immune theory is presented.
通过借鉴生物免疫系统中的克隆选择原理和记忆机制，提出了一种人工免疫c -均值混合聚类算法。
Inspired by the clone selection principle and memory mechanism of the vertebrate immune system, a hybrid algorithm combining C-means algorithm and artificial immune algorithm is presented.
针对模糊c均值算法与粒子群算法的不足，提出了一种基于粒子群算法和模糊c—均值算法的混合聚类算法。
To avoid the shortcomings of FCM and Particle Swarm Optimization algorithm, new hybrid clustering algorithm based on PSO and FCM algorithm is proposed.
该混合算法包括优选聚类算法和新型二阶学习算法。
The hybrid algorithm includes an optimal selection cluster algorithm and a second-order algorithm.
首先该文利用模糊C均值聚类和可能性C均值聚类的优点，设计出一种混合C均值聚类算法。
Firstly, the advantages of fuzzy C-means clustering and possibilistic C-means clustering are utilized in this paper. We design a new hybrid C-means clustering accordingly.
提出了客户时间序列的加权处理方法，并应用客户时间序列的统计特征作为聚类特征向量，采用混合式遗传算法对客户聚类，使每一类客户具有相似的时序特征。
A weighted method of customer's time series is proposed and statistical features of time series are adopted for customer clustering, which make each group of customers have similar sequence feature.
该文提出了一种基于K近邻加权的混合C均值聚类算法。
A new weighted hybrid C-means clustering based on the K-nearest-neighbour rule is presented in this paper.
首先该文利用模糊C均值聚类和可能性C均值聚类的优点，设计出一种混合C均值聚类算法。
Firstly, the advantages of fuzzy C-means clustering and possibilistic C-means clustering are utilized in this paper.
现有的数据流聚类算法无法处理高维混合属性的数据流。
Existed data stream clustering algorithms can not deal with the data stream with high-dimensional heterogeneous attributes.
基于混合模型聚类算法是众多聚类算法的一种。
Mixed model-based cluster algorithm is one of the numerous cluster algorithms.
然后以K近邻规则为基础，计算出样本集的加权矩阵，最后得到基于K近邻加权的混合C均值聚类算法。
And then based on the K-nearest-neighbour rule, the weighted matrix of samples is computed. Lastly, weighted hybrid C-means clustering based on the K-nearest-neighbour rule is presented.
该方法利用Curvelet多尺度几何分析后信号的稀疏性特点，采用了C - means聚类方法寻求混合矩阵估计值，把该估计值作为算法初始值。
According to signals sparsity by Curvelet transform, the mixed matrix can be estimated with C-means cluster analysis, and the estimated value is looked as initial value of BSS algorithm.
该方法利用Curvelet多尺度几何分析后信号的稀疏性特点，采用了C - means聚类方法寻求混合矩阵估计值，把该估计值作为算法初始值。
According to signals sparsity by Curvelet transform, the mixed matrix can be estimated with C-means cluster analysis, and the estimated value is looked as initial value of BSS algorithm.
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