搜索资源列表
kmeans
- Hadoop的k-means算法，分布式K-means-Hadoop-k-means algorithm, distributed K-means
Kmeans
- 使用Java实现K-means（C均值）聚类算法-Using Java to achieve K-means (C mean) clustering algorithm
Kmeans
- K-均值聚类算法，是一种随机选取数个数据中心进行点聚类处理进而生成分类的数据挖掘算法，具有很好的学习功能。-K-means clustering algorithm is a randomly selected number of data center point clustering process thereby generating classification data mining algorithms, with good learning function.
Fk-menas
- 基于Hadoop的模糊K-Means算法，在MapReduce框架下编写，经集群测试成功运行。压缩包中包含源码和实验数据-Hadoop-based fuzzy K-Means algorithm, written in the MapReduce framework, through the cluster test run successfully. Compressed package contains the source code and experimental data
src
- 聚类算法，包括ISOdata以及K-Means。在实验报告中详细分析了一下实验的结果-Clustering algorithms, including ISOdata and K-Means. In a detailed analysis of the experimental report about the results of the experiment
kmeans_report
- Java 实现k-means 聚类算法，分别以迭代次数及分配不再发生变化为算法终止条件，用图片作为数据集，比较运行时间-Java implementation of k-means clustering algorithm, respectively, and the distribution of the number of iterations of the algorithm terminates no change in the conditions, with a picture (o
Kmeans
- k均值聚类算法代码, k均值聚类算法代码-k-means clustering algorithm code, k-means clustering algorithm code
_k_means_picture
- K-means聚类算法 用于图像处理 JAVA语言编写，聚类中值算法可运行-k-means clustering algorithm image processing
CBIR
- Content based image retrieval in java using k-means clustering and haar wavelet transform
K_Means
- k-means 算法的工作过程说明如下：首先从n个数据对象任意选择 k 个对象作为初始聚类中心；而对于所剩下其它对象，则根据它们与这些聚类中心的相似度（距离），分别将它们分配给与其最相似的（聚类中心所代表的）聚类；然后再计算每个所获新聚类的聚类中心（该聚类中所有对象的均值）；不断重复这一过程直到标准测度函数开始收敛为止。一般都采用均方差作为标准测度函数. k个聚类具有以下特点：各聚类本身尽可能的紧凑，而各聚类之间尽可能的分开。下面给出我写的源代码。-work process k-means al
MyTextCluster
- 实现k-means算法的文本分类，用java代码实现的，希望对大家能有帮组-k-means cluster
SimpleKMeans
- k-means 代码实现， 在My Eclipse 中运行-k-means ， realized by Java
Iris
- K-means算法，Java语言实现Iris数据集分类-k-means implement
Kmeans
- k-means算法的实现，k-means算法的实现k-means算法的实现，k-means算法的实现-k-means compeletek-means compeletek-means compeletek-means compelete
kmeans
- k-means clustering is a method of vector quantization, originally signal processing, that is popular for cluster analysis in data mining. k-means clustering aims to partition n observations into k clusters in which each observation belongs to the clu
CanopyExm
- Canopy聚类算法是一个将对象分组到类的简单、快速、精确地方法。每个对象用多维特征空间里的一个点来表示。这个算法使用一个快速近似距离度量和两个距离阈值 T1>T2来处理。 Canopy聚类算法能快速找出应该选择多少个簇，同时找到簇的中心，这样可以大大优化 K均值聚类算法的效率 。-Canopy is a clustering algorithm to group objects into simple categories, fast, accurate method. Each obj
dlib-18.14.tar
- 机器学习的范畴，包括SVMs (based on libsvm), k-NN, random forests, decision trees。可以对任意的数据操作-Its focus is on supervised classification with several classifiers available: SVMs (based on libsvm), k-NN, random forests, decision trees. It also performs feature sel
julei
- TFIDF产生文本权重，在用K-means算法进行聚类。方法简单，可供相关人员参考继续深入学习-TFIDF generated text weights in with K-means clustering algorithm. The method is simple, the relevant officers for further study
KMeans
- K-means算法是硬聚类算法，是典型的基于原型的目标函数聚类方法的代表，它是数据点到原型的某种距离作为优化的目标函数，利用函数求极值的方法得到迭代运算的调整规则。-K-means clustering algorithm is hard, is a typical prototype-based clustering method on behalf of the objective function, it is a method of data points to a certain di
k-means
- this a very usefull code-this is a very usefull code