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一种新的聚类分析距离算法
- K均值是一个预先知道类数的算法，需要具备专业知识，不现实。本文提出一个确定类数的方法。-K is a means to know in advance the number of categories algorithm, requires expertise and unrealistic. This paper presents a number of categories to determine the method.
KMEANS
- 输入：聚类个数k，以及包含 n个数据对象的数据库。输出：满足方差最小标准的k个聚类。处理流程： （1）从 n个数据对象任意选择 k 个对象作为初始聚类中心. （2）根据每个聚类对象的均值（中心对象），计算每个对象与这些中心对象的距离；并根据最小距离重新对相应对象进行划分；（3）重新计算每个（有变化）聚类的均值（中心对象） （4）循环（2）到（3）直到每个聚类不再发生变化为止-Input: number of clusters k, and n data object contains a
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
iris_php_mean
- iris采用聚类算法分类，php代码实现。iris_train2.php用于训练数据，iris_test.php用于测试数据。-a kind of k-means,php programming
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
DataStructTest
- 使用k-means + tf-idf 实现简易的文本分类算法,可直接运行- U4F7F u7528k-means+ tf-idf u5B9 u7B0 u7B80 u6613 u7684 u6587 u672C u5206 u7C7B u7B97 u6CD5, u53EF u76F4 u63A5 u8FD0 u884C
python-cure-implementation-master
- Python实现的cure聚类算法和K-means算法(python-cure-Kmeans-implementation)
attachments
- K Means clustering with multiple attributes taken differently
Kmeans.ipynb
- K means CLustering algorithm
7303
- K-means clustering algorithm based on the PSO, PLS PLS toolbox, Calculate the multifractal trend fluctuation analysis.
kmeans
- kmeans算法的java实现，搬运，大家可以参考一下(Kmeans algorithm java implementation, handling, we can refer to)
Hadoop-Kmeans
- hadoop平台下的K-means算法的实现(Implementation of K-means algorithm under Hadoop platform)