| 1. | On document clustering based on fuzzy c - mean algorithm
均值算法文档聚类问题的研究 |
| 2. | Image segmentation using the method of pyramidal fuzzy c - mean cluster
均值聚类图像分割方法 |
| 3. | Escaped toll analysis of etc system customer data based on fuzzy c - means clustering
系统客户的逃费分析研究 |
| 4. | Image segmentation algorithm based on simulated annealing and fuzzy c - means clustering
均值聚类相结合的图像分割算法 |
| 5. | Clone principle is led into evolutionary computing , and a hybrid algorithm is combining antibody clone strategy with fuzzy c - means clustering method is given . it is used in intrusion detection
提出将人工免疫与模糊c -均值聚类技术相结合进行聚类，从而实现对异常行为的检测的算法。 |
| 6. | The parameter sensitivity of induction motor load model is studied . a new approach based on fuzzy c - means clustering is proposed for the classification of dynamic load characters
分析了感应电动机综合负荷模型参数灵敏度，介绍了模糊聚类的分类方法，提出了基于模糊c均值聚类的负荷特性分类方法。 |
| 7. | Fuzzy c - means algorithm is used to cluster the production data in batch annealing process , then the exponent least square algorithm is used to get the relationship between cooling time and weight of clusters
采用模糊c均值聚类方法对退火生产数据进行处理，再基于得到的聚类数据点进行指数最小二乘回归。 |
| 8. | We use image quantization and image enhancement techniques to preprocess the polsar data . we then use the polarimetric information and fuzzy c - means ( fcm ) clustering algorithm to classify the preprocessed images
我们使用图像量化和图像增强技术对原始sar数据进行预处理;在特征空间中利用极化信息、使用模糊c均值( fcm )算法对预处理后的sar图像进行分类。 |
| 9. | Firstly , the initial values of cluster are obtained by hough transform , which consider the linearity and continuity , then the premise and consequent parameters are identified based on fuzzy c - means and recursive least square
首先利用hough变换的方法得到聚类中心的初始值，然后通过模糊c -均值聚类法辨识前提参数，采用递推最小二乘辨识模糊模型的结论参数。 |
| 10. | So a framework of vehicle faults diagnosis based on rs and nn has been proposed and illustrated in an application for the faults diagnosis of gearbox . continuous attributes are discretized by means of fuzzy c - means , kohonen neural network and k - means
分析了粗糙集理论和神经网络技术在故障诊断应用中的优点和缺点，阐明了二者结合的必然性，提出将二者结合起来的车辆故障诊断数据挖掘系统框架。 |
