搜索资源列表
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
- 实现对用户进行分类，并用图形化界面显示分类结果-Implement classifying users and graphical interface according to classification results
textcluster
- java版的k-means算法，实现文本聚类功能-the k-means algorithm in java
Cluster
- 聚类算法的java实现，包括K-means（基于划分聚类），DBSCAN(基于密度聚类）-Clustering algorithm ， achieved by java, including K-means (based on the division clustering), DBSCAN (density-based clustering)
KMeans2
- kmeans算法的Java实现。算法接受参数 k 然后将事先输入的n个数据对象划分为 k个聚类以便使得所获得的聚类满足:同一聚类中的对象相似度较高 而不同聚类中的对象相似度较小。-k means algorithm is implemented in Java. Receiving algorithm parameter k and n data objects entered beforehand into k clusters in order to satisfy such cluste
WawaKMeans
- WawaKMeans的算法实现，用Wawa实现K-means聚类算法与MapReduce实现的算法进行对比-WawaKMeans algorithm implementation, using K-means to achieve Wawa clustering algorithm and MapReduce implementation of the algorithm to compare
chenxuejing
- 基于kmeans与遗传算法结合的代码，对于新手很有作用，编码采用二进制-Based on k means algorithm combined with genetic code, useful for the novice action, binary encoding
kmeans
- k means算法，从网上下载的，尽情享用-k means algorithm, downloaded the Internet, enjoy
project3
- text mining, bayes k-means
clusterTest
- 用java语言实现k均值聚类的代码demo，可直接运行,无需调试。-Using java language k-means clustering code demo, it can be run directly without debugging.
src
- 实现在Hadoop平台上分布式环境上的K-means聚类，随机选取中心点后进行分类-Implementing K-means clustering on a distributed environment on the Hadoop platform, sorting randomly after selecting the center point