搜索资源列表
K-means_clustering_demo
- K-均值聚类算法 vc++图形演示程序-K-means clustering algorithm c++ demo program
K-means
- K-means K聚类算法的C++语言实现，里面附有使用文档和实例，在VS2005上实现的-K-means K clustering algorithm C++ language, which accompanied with documentation and examples implemented on the VS2005
k-MeansJava
- k-Means算法Java实现 数据挖掘中经典的Kmeans算法设计与实现-k-Means Algorithm for Data Mining Java implementation of the classic algorithm design and implementation Kmeans
k-means
- k均值聚类算法源码 聚类算法学习的实例功能-k-means cluster algorithm
k
- k平均聚类所谓k均值聚类方法是一种无监督的学习算法，它能用已知类数的数据聚类和预测。-k-average clustter
k-means
- k-means算法实现WEB搜索，用模拟退火，对K-means聚类算法进行数据挖掘-k-means algorithm WEB search
K-means
- kmeans算法代码，已经通过验证，可以正常使用，没有病毒
k-means
- k-means算法 ，应 用 于 数 据 挖 掘 中。-k-means algorithm is applied to data mining
K-Means
- 传统K-means算法程序，希望对你有帮助，加油，朋友们-Traditional K-means algorithm, you want to help, and refueling friends
k-means
- 完成三维空间的k-means算法，其中的k是由执行人员自己设定的-Completion of the three-dimensional space of k-means algorithm, where k is the Executive set their own
k-means
- k-means 算法，由c语言实现简单的聚类操作。-k-means algorithm, the c language simple clustering operation.
K-means
- 模式识别中K-means算法的基本实现，可实现2位向量的分类。-the basic realization of Pattern Recognition K-means algorithm
K-means
- C C++ K-means算法的实现，希望能帮上大家。-C C++ K-means algorithm, hope to help you
K-Means
- K-Means算法matlab实现,使用自己生成的数据文本-K-Means Alogrithm
kmeans
- Scala实现多个特征量的k-means算法(K-means algorithm for implementing multiple feature quantities in Scala)
K-means
- 使用k-means算法对图像进行分割，并利用遗传算法对k-means算法加以改进(The k-means algorithm is used for the segment of images, and the genetic algorithm is used to improve the k-means algorithm)
pres
- 三种分类器：决策树分类器，k-NN分类器和k-means分类器的运行时间以及运行准确率的比较。(Three kinds of classifiers: decision tree classifier, k-NN classifier and K-means classifier running time and accuracy comparison.)
K_Means_Cluster
- K-means 算法(K-means)
k-means-master
- just a silly k-means implementation in MATLAB. 简单的用MATLAB实现的kmeans算法(just a silly k-means implementation in MATLAB.)
K---MEANS
- 随机生成1000个二维坐标点并用K-means算法计算聚类结果(1000 two-dimensional coordinate points are generated randomly and the clustering results are calculated by K-means algorithm)