搜索资源列表
h2
- 最新纯JAVA数据库，支持集群，支持私有内存数据库，-latest pure Java database, clustering support, the private support memory database
Weka-3-2
- Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clusteri
weka-src.rar
- Weka，一个数据挖掘工具。功能包括：分类、聚类和关联规则等等。这是该开源软件的源代码，版本为3.5.7,Weka, a data mining tool. Features include: classification, clustering and association rules, etc.. This is the open source software source code, version 3.5.7
weka-3-6-1
- Weka是一个超强功能的machine learning开发包-Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, clas
DBSCAN
- 基于密度的聚类算法 DBSCAN java-Density-based clustering algorithm DBSCAN java
suanfashixian
- 通过聚类分析算法实现可以得到想要的聚类结果-Clustering algorithm to use java analysis
CreateTree
- Fuzzy C means clustering
kmeans
- 数据挖掘kmeans图像聚类实验代码 用 VC 或 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（自己选择）作为数据集，比较运行时间（画出时间与像素点的关系曲线图，因此须用多幅像素个数不同的图片进行实验） 提交实验报告与源代码。-VC or Java k-means clustering algorithm, were no longer change the number of iterations and the allocation
kmeans_report
- 数据挖掘kmeans图像聚类实验,用 VC 或 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（自己选择）作为数据集，比较运行时间（画出时间与像素点的关系曲线图，因此须用多幅像素个数不同的图片进行实验） 提交实验报告与源代码-Data mining kmeans image clustering experiments, using VC or Java implementation of k-means clustering algorith
Kmeans_medoids
- k-means和k-mediods的JAVA实现。直接读取文档数据，适用于二维数据。-k-means and k-Medoids clustering algorithm JAVA implementation. Document data read directly,suitable for two-dimensional data.
wheats-master
- 基于网格的聚类算法，经典的clique算法的java实现，(Grid-based clustering algorithm, the classic clique algorithm java implementation,)
K-means
- Kmeans聚类算法的java实现方法，比较简洁。(Java implementation of Kmeans clustering algorithm)
moa-release-2016.04
- MOA Machine Learning for Streams。它包括一系列机器学习算法（分类，回归，聚类，异常值检测，概念漂移检测和推荐系统）以及评估工具。与WEKA项目相关的MOA也是用Java编写的，同时扩展到更严苛的问题。 https://moa.cms.waikato.ac.nz/(It includes a series of machine learning algorithms (classification, regression, clustering, outlier